Rootly — answer pack ==================== > Self-contained context for answering questions about Rootly: identity, > products, pricing, security, comparisons, customer evidence, FAQ, and the > overview of every content pillar. Generated at build; every URL is canonical. > > Index/manifest: https://rootly.com/llms.txt > Deep topic bundles: https://rootly.com/llms/.txt (listed at the end of this file) > Single pages: append `.md` to any guide article URL --- # What Rootly is > Rootly is the AI incident management platform that unifies on-call, incident response, retrospectives, and status pages - built for fast-moving engineering teams to resolve incidents faster with Slack/Teams-native workflows, automation, and AI assistance. Rootly is an AI-native incident management platform for fast-moving engineering teams. It brings on-call alerting/paging, incident response workflows in Slack or Microsoft Teams, post-incident learning (retrospectives), and status pages into one place. Teams use Rootly's automations and AI to coordinate responders, keep stakeholders updated, and capture timelines and follow-ups - so incidents resolve faster and learning compounds over time. Category: Incident Management (AI-native on-call + incident response + retrospectives + status pages + AI SRE) Trusted by: DoorDash, NVIDIA, Figma, Dropbox, SoFi, Replit, Stack Overflow, Superhuman, Clay, Wealthsimple, Glean, Canva, TripAdvisor, Trivago, WIX, Brex, Sailpoint Website: https://rootly.com Also: https://www.linkedin.com/company/rootlyhq/ · https://github.com/rootlyhq · https://www.crunchbase.com/organization/rootly · https://x.com/rootlyhq --- # Products - **Rootly On-Call** — Alerts, paging, schedules, escalations, coverage workflows, live call routing, heartbeats, and mobile apps https://rootly.com/on-call - **Incident Response for Slack** — Slack-native incident workflows with automations and AI assistance https://rootly.com/incident-response-slack - **Incident Response for Microsoft Teams** — Teams-native incident workflows with automations and AI assistance https://rootly.com/incident-response-microsoft-teams - **Incident Response for Google Chat** — Google Chat-native incident workflows with automations and AI assistance https://rootly.com/incident-response-with-google-chat - **Retrospectives** — Post-incident learning with automated timelines, templates, action items, and reliability metrics https://rootly.com/retrospectives - **Rootly AI SRE** — AI investigation across the incident lifecycle - root cause analysis with confidence scores, summaries, drafting, and suggested fixes https://rootly.com/ai-sre - **Rootly agents** — AI agents at every stage of an incident - investigation, paging and briefing, live notes on the bridge, fixes drafted as pull requests, updates for every audience, and the retrospective, with a person approving every change https://rootly.com/agents - **Status Pages** — Public, private, and internal status pages with workflow-driven updates https://rootly.com/status-pages - **Mobile App** — On-call paging, alerts, and AI-assisted incident response on iOS and Android https://rootly.com/mobile --- # Pricing Published list prices; every tier below is quoted from /pricing. - Incident Response is $20 and On-Call is $20 per user per month, or $40 for both at list price. AI SRE requires Incident Response and is priced on request, Enterprise pricing is custom, and the free trial typically lasts two weeks. - Essentials: $20 per user, per month for Rootly Incident Response and $20 for Rootly On-Call. They are separate products that each work standalone; together they are $40 per user, per month at list price. - Enterprise: quoted, not list-priced. Adds custom/dynamic forms, custom incident types, private incidents, native secrets management, custom data retention, audit logs, advanced workflows, and SSO + SAML + SCIM. - Rootly AI SRE: an additional product that requires Incident Response. Quoted, not list-priced, and included with Enterprise. - AI agents (@Rootly, AI Scribe, and AI retrospectives) and Status Pages come with Incident Response; there are no per-feature add-ons. - Full tier detail and the feature matrix: https://rootly.com/pricing - **Pricing** — Per-product list prices, AI SRE and Enterprise quotes, trial and startup pricing https://rootly.com/pricing --- # Security, trust and AI data handling - **Security Overview** — Security program, certifications, and data protection https://rootly.com/security --- # Integrations - **Integrations Hub** — Browse all available integrations https://rootly.com/integrations - **Terraform Provider Guide** — What the rootlyhq/rootly provider manages, with HCL for schedules, escalation policies, services, and workflows https://rootly.com/integrations/terraform --- # Competitor comparisons - **Rootly comparisons** — Index of the comparison pages below, each with the date its claims were last verified https://rootly.com/alternatives - **Rootly vs PagerDuty** — All-in-one incident management vs paging-only, at roughly half the cost https://rootly.com/alternatives/rootly-vs-pagerduty Markdown: https://rootly.com/alternatives/rootly-vs-pagerduty.md - **Rootly vs Opsgenie** — Migrating off Opsgenie before its April 2027 end-of-life https://rootly.com/alternatives/rootly-vs-opsgenie Markdown: https://rootly.com/alternatives/rootly-vs-opsgenie.md - **Rootly vs Jira Service Management** — Running incidents vs tracking them as tickets https://rootly.com/alternatives/rootly-vs-jira-service-management Markdown: https://rootly.com/alternatives/rootly-vs-jira-service-management.md --- # Who uses Rootly Named customer evidence; each story is a separate page. - **Customers Directory** — All customer stories https://rootly.com/customers Named customer stories (19): - Achievers: https://rootly.com/customers/achievers - Capa: https://rootly.com/customers/capa - Clay: https://rootly.com/customers/clay - Cora: https://rootly.com/customers/cora - CTSO Central: https://rootly.com/customers/ctso-central - KnowBe4: https://rootly.com/customers/knowbe4 - Lucidworks: https://rootly.com/customers/lucidworks - Momentum: https://rootly.com/customers/momentum - Motive: https://rootly.com/customers/motive - Poll Everywhere: https://rootly.com/customers/poll-everywhere - Reactiv: https://rootly.com/customers/reactiv - Replit: https://rootly.com/customers/replit - Roller: https://rootly.com/customers/roller - SpotDraft: https://rootly.com/customers/spotdraft - Superhuman: https://rootly.com/customers/superhuman - UNiDAYS: https://rootly.com/customers/unidays - Upstart: https://rootly.com/customers/upstart - Wealthsimple: https://rootly.com/customers/wealthsimple - Webflow: https://rootly.com/customers/webflow --- # Research Rootly benchmark report on ~500K incidents at 650+ companies; the PDF is the same page, printed. - **How to achieve great incident management** — Benchmark report and opinionated guide built on a sample of Rootly data (350K+ incidents at 650+ companies) - on-call, paging, mitigation, updates, retrospectives, MTTR and AI, with what the best quarter of teams achieve (PDF: https://rootly.com/assets/reports/rootly-ai-great-incident-management.pdf) https://rootly.com/great-incident-management --- # Also published - **Home** — Main website https://rootly.com/ - **Enterprise** — Rootly for large engineering organizations https://rootly.com/enterprise - **Startups** — Rootly for startups https://rootly.com/startups --- # Content pillars — overviews in full The complete text of each pillar overview. Supporting articles are indexed further down, with their full text in the matching bundle. --- # What is AI SRE? A practical guide to AI-powered incident response URL: https://rootly.com/ai-sre-guide Pillar: AI SRE Learn how AI SRE agents investigate incidents, analyze likely root causes, and support remediation. Explore capabilities, limitations, and adoption steps. AI SRE is officially a category now. In January 2026, Gartner published its first Market Guide for AI Site Reliability Engineering Tooling. Gartner now lists the category as [“AI Site Reliability Engineering Tooling”](https://www.gartner.com/reviews/market/site-reliability-engineering-tooling) and defines it as a market that “enables and supports the adoption of SRE practices, and focuses on improving reliability, resilience and the customer experience.” Rootly helped popularize the term, so Gartner’s recognition should feel like pure validation. It does... and it makes the problem with the name harder to ignore. I recently wrote [“Borrowed gravity: words worth changing”](https://rootly.com/blog/borrowed-gravity-words-worth-changing) with SRE Sebastian Vietz. His objection was simple: the name shrinks SRE to its most visible incident-time task. SRE also means reliability design, SLOs, error budgets, capacity planning, risk reduction, observability, and making systems more dependable. An investigation tool can be useful. It still is not an SRE. That leaves me in an awkward but useful place. I want people to find this category because I think the underlying tools can help. I do not want the category name to become permission to underinvest in SREs or pretend we have stuffed an experienced engineer into a box. What I have found is a narrower, more credible use. At 2:47 a.m., the evidence behind an alert is scattered across telemetry, deployments, code, runbooks, ownership catalogs, tickets, and old incident channels. Collecting it is repetitive work at the moment an on-call engineer has the least attention to spare. I use “AI SRE” because that is the term people search for, but with a firm boundary. Here it means software that gathers operational evidence, tests likely causes, and recommends or performs tightly guarded actions. It can shorten the path to a decision. It does not replace the people accountable for reliability. ## Key takeaways Here is my short version: - AI SRE is an incident investigation aid, not a substitute for site reliability engineering. - Its most useful output is not a confident answer. It is an evidence-backed hypothesis that a responder can inspect, challenge, and act on. - The quality of the result depends on the quality of the underlying telemetry, ownership, runbooks, change data, and permissions. - Adoption should move from read-only investigation to approval-gated action, with autonomy granted per action rather than to the system as a whole. - Measure whether the tool supplies useful context, identifies the right owner, and supports safe decisions, not only whether a single time-based metric moved. ## What is AI SRE? In practical terms, AI SRE uses language models and other analysis techniques to investigate production incidents. It connects an alert with logs, metrics, traces, recent changes, code, ownership, runbooks, and previous incidents. It can then rank likely causes, suggest the next check, recommend a mitigation, and record what happened. You will also see this capability described as **[AI incident management](https://rootly.com/ai-sre-guide/ai-incident-management)**, **AI incident response**, **AI incident investigation**, or **automated root cause analysis**. The terms overlap, but they are not identical: - **Observability tools** collect and expose signals about a system. An AI investigation layer works across those signals and other operational systems to help decide what to inspect next. - **Deterministic automation** follows a predefined rule: when X happens, do Y. AI-assisted investigation is useful when the path is less structured, but that flexibility also makes its output less predictable. - **AIOps** is a broad category spanning event correlation, anomaly detection, noise reduction, and IT operations. AI SRE is usually positioned more narrowly around reliability and incident workflows. The history and ambiguity of that distinction are covered in [“What does AIOps mean for SREs? It’s complicated”](https://rootly.com/blog/what-does-aiops-mean-for-sres-it-s-complicated). - **An SRE** is a person practicing an engineering discipline. No product can own an SLO, negotiate an error budget, understand every business risk, or take organizational accountability for reliability. I find one question much more useful than “Can this replace an SRE?”: **Can this system give an SRE trustworthy context and a safer next move while an incident is unfolding?** For a deeper treatment of the terminology and boundaries, see [AI SRE concepts](https://rootly.com/ai-sre-guide/concepts). ## Why traditional SRE is breaking I do not think traditional SRE is necessarily breaking, but one assumption about how it is practiced is. The principles still hold: define reliability objectives, instrument the system, reduce toil, learn from failure, and use automation where it makes operations safer. What is breaking is the assumption that a responder can manually assemble all the context behind a modern service quickly enough, every time, while under pressure. Three conditions make incident investigation especially expensive: 1. **Evidence is fragmented.** The alert may be in one tool, the useful trace in another, the deployment event in a third, and the explanation for an odd configuration choice in a six-month-old incident channel. 2. **Ownership is ambiguous.** A customer-facing symptom can cross several services and teams. The engineer who gets paged may own the symptom but not the change that produced it. 3. **Working memory is the bottleneck.** Responders have to compare timestamps, dependencies, logs, diffs, known failure modes, and mitigation risks while coordinating with other people. AI does not remove these problems. Weak operational foundations put a hard ceiling on it. [Ganesh Datta, co-founder and CTO of Cortex, argues](https://rootly.com/humans-of-reliability/ganesh-datta-cortex) that AI amplifies the systems beneath it. Good monitors, usable logs, accurate ownership, and maintained runbooks help. Missing or stale context gives it more ways to be confidently wrong. ## Core capabilities of AI SRE systems ![Illustration for “Core Capabilities of AI SRE Systems”](https://rootly.com/assets/ai-sre-guide-the-complete-guide-to-ai-sre-transforming-site-reliability-.avif) After looking at enough feature lists, I stopped finding them useful. They all sound the same: detect, diagnose, correlate, predict, remediate. I prefer to evaluate an AI SRE system as an investigation loop and ask what a responder can inspect at each step. | Stage | Useful behavior | Evidence a responder should see | | --- | --- | --- | | Collect | Pull the signals relevant to the affected service and time window | Source links, timestamps, query scope, and missing data | | Correlate | Connect symptoms with changes, dependencies, and similar incidents | Why two events may be related, not merely that they occurred together | | Form hypotheses | Rank plausible explanations for the observed behavior | Supporting and conflicting evidence for each hypothesis | | Test | Run targeted queries or checks that distinguish one hypothesis from another | The check performed, its result, and how the ranking changed | | Recommend | Propose the smallest useful diagnostic or mitigating action | Expected outcome, risk, permissions, and rollback path | | Act | Execute only within an explicit policy and approval boundary | Approver, action log, scope, result, and abort condition | | Preserve | Add the investigation trail to the incident record | Evidence, decisions, corrections, and unresolved questions | I am especially cautious when a product claims it has “found the root cause.” Root cause is rarely a fact a model can read directly from a dashboard. During an incident, the more defensible output is a **likely-cause hypothesis supported by evidence**. I want the system to show how it got there and what would disprove it. Detection can be part of the workflow, but it is not investigation. If an alert has paged the on-call engineer, the incident has been detected. The value from that point is useful context, a credible hypothesis, the correct owner, and a safe mitigation—not a retroactive claim that investigation time was MTTD. ## What is an AI SRE agent and what can it do today? An AI SRE agent is software that investigates a production incident alongside the on-call engineer: it gathers the relevant signals, forms hypotheses about the likely cause, tests them with targeted queries, and recommends the next check or mitigation, with the evidence attached. The useful ones work inside the tools responders already use and act only within the permissions a human sets. Rootly AI SRE is one example: it starts investigating the moment an alert fires and ranks probable causes with confidence scores. Here is what that looks like, using Rootly AI SRE as the concrete example: - **Starts when the alert fires.** The investigation begins before the responder has finished reading the page, pulling in related alerts, recent deploys and code changes, and past incidents on the same service. - **Ranks probable causes with confidence scores.** Each hypothesis comes with the evidence for it, so the responder can check the reasoning instead of trusting a verdict. - **Suggests fixes and next steps with the reasoning shown.** The recommendation arrives with how and why it was reached. - **Matches similar past incidents.** It surfaces earlier incidents that look like this one, the fixes that worked and the people who handled them. - **Works in the incident channel.** Tagging @Rootly in Slack gives a responder a personalized catch-up summary, and it can draft communications, assign tasks and set severity from the same thread. - **Captures the incident bridge.** A meeting bot transcribes the call in real time so decisions don't get lost. - **Writes the updates and the retrospective.** It drafts status updates pitched at responders, leadership and customers, then generates the retrospective from the full timeline. - **Reaches the editor.** The Rootly MCP server brings incident context into Cursor, Windsurf and Claude. What an AI SRE agent can't do matters as much. It can't own an SLO or make the reliability trade-offs an SRE is accountable for. It can't be more right than the context it is given, which is why the [limitations section](#current-limitations-and-considerations) below matters. And it shouldn't take an action nobody approved. I'd judge any agent, ours included, on whether it shows its evidence and stays inside that boundary. How an AI SRE agent differs from the AIOps tools that sit upstream of it is covered in [AI SRE vs AIOps](https://rootly.com/ai-sre-guide/ai-sre-vs-aiops). ## How LLMs power incident operations What makes language models interesting for incident operations is not that they “know reliability.” It is that so much of the relevant context is semi-structured or written for humans. Runbooks, code diffs, deployment notes, incident timelines, tickets, and chat transcripts do not fit neatly into one query language. An LLM can help by: - translating a responder’s question into searches across operational tools; - summarizing the evidence returned by those tools; - comparing a current incident with previous incidents and documented failure modes; - generating competing hypotheses and suggesting checks that separate them; - drafting status updates, handoffs, and incident records from facts already established. I think of the model as an interface over operational truth, not the source of it. Telemetry, code, change records, identity systems, and human decisions remain the sources of truth. The model selects, compares, and explains evidence from them. That is why I do not put much weight on general-purpose model benchmarks here. Incident investigation requires reading noisy logs, selecting relevant context, using tools correctly, and staying within permission boundaries. Rootly’s [SRE-focused benchmark work with Groq OpenBench](https://rootly.com/blog/rootly-joins-groq-openbench-with-an-sre-focused-benchmark) evaluates those operational tasks instead of generic question answering. ## Under-the-hood mechanics Behind the agent diagrams, an AI SRE system usually sits above the tools a team already uses. I look for six practical parts: 1. **Connectors** retrieve scoped data from observability, cloud, deployment, code, catalog, ticketing, and incident systems. 2. **Retrieval and normalization** organize the returned context around the affected services, entities, and time window. 3. **A reasoning and tool-use layer** decides which query or diagnostic check to run next. 4. **Policy and identity controls** determine what the system may read, recommend, or change. 5. **An action workflow** handles approvals, execution, rollback, and audit logging. 6. **Evaluation and feedback** record whether the context was useful, the hypothesis held up, and a human corrected the result. Whatever terminology a vendor uses, I expect the internal loop to keep returning to five questions: - What changed? - What evidence supports this explanation? - What competing explanation still fits? - What check would disprove the leading hypothesis? - What is the smallest reversible action that would reduce impact or uncertainty? The [AI SRE architecture guide](https://rootly.com/ai-sre-guide/architecture) covers this system in more detail, while the [integrations guide](https://rootly.com/ai-sre-guide/integrations) explains the data and permission surface. ## The 2:47 AM test: where AI SRE shines This is the test I keep coming back to. Almost any AI incident tool can look impressive in a prepared demo at 2:00 p.m. I care about what it gives a tired responder at 2:47 a.m., when the evidence is incomplete and the safe next move is not obvious. Imagine a checkout alert fires at 2:47 a.m. Error rates have climbed in one region, but the dashboard does not explain why. The on-call engineer opens traces, searches logs, checks deploys and feature flags, finds the owning team, and looks for a similar incident. The toil is in assembling all of it while customers wait. A useful machine-assisted investigation might return this: | Finding | Evidence | Effect on the investigation | | --- | --- | --- | | Checkout failures began three minutes after a connection-pool configuration rollout | Deployment record and error-rate timeline | Raises the configuration change as a hypothesis | | Failures appear only on instances with the new value | Instance configuration and request traces | Strengthens the hypothesis | | The payment provider’s latency is normal | Dependency telemetry and status data | Weakens a plausible competing hypothesis | | A previous incident produced the same exhaustion pattern | Linked incident record and matching log signature | Suggests a known diagnostic check and mitigation | | Rolling back one canary instance is available and reversible | Deployment permission, rollback plan, and health criteria | Offers a bounded next action | This is what “good” looks like to me. The system has not discovered an unquestionable “root cause.” It has produced an evidence packet: a leading hypothesis, a competing hypothesis it tested, the sources behind both, and a limited action that can generate more evidence or reduce impact. The responder still reviews the scope, checks that rollback will not corrupt in-flight work, approves the canary, and watches the success and abort conditions. If the evidence conflicts or the action cannot be bounded safely, the right output is: **insufficient evidence, no action taken, page the service owner.** [Eran Kampf, co-founder of Monday.com, uses AI for initial triage](https://rootly.com/humans-of-reliability/eran-kampf) across runbooks, logs, and Kubernetes context while keeping people in the loop according to the judgment and risk involved. I trust that standard more than a demo in which the agent is always right. ## Human in the loop reliability model I have heard “human in the loop” used loosely enough that I now want to see the loop. It should describe a real control system, not a reassuring label added to an autonomous workflow. Authority should come in stages: 1. **Read-only investigation:** the system gathers evidence and shows its work. A human performs every operational action. 2. **Recommended action:** the system proposes a diagnostic or mitigation with rationale, scope, and rollback steps. 3. **Approval-gated execution:** an authorized responder approves a specific action before the system runs it. 4. **Policy-bounded execution:** the system may perform a narrow class of pre-approved actions when explicit conditions are met, with complete logging and automatic abort criteria. These levels should apply to individual actions, not the product as a whole. Querying logs, restarting a stateless canary, failing over a database, and rotating credentials do not carry the same risk. “Low risk” means bounded, reversible, pre-approved, observable, and backed by a tested rollback, not merely familiar. [Dana Lawson, CTO at Netlify, warns](https://rootly.com/humans-of-reliability/dana-lawson-netlify) that AI tools have looser boundaries than deterministic automation. Calling one an SRE can make the profession sound reducible to a set of incident tasks. A credible implementation has to preserve both technical control and respect for the people accountable for the system. The [AI SRE maturity model](https://rootly.com/ai-sre-guide/maturity-model) provides a fuller path from assisted investigation to carefully bounded automation. ## Current limitations and considerations This is where I think the guide has to get blunt. AI SRE agents can be useful today, but they work inside strict limits. I trust products more when they expose those limits instead of hiding them behind a confidence score. | Failure mode | What a credible system should do | | --- | --- | | Missing telemetry or inaccessible systems | Identify the gap and reduce the scope of its conclusion | | Stale runbooks or ownership data | Show the source and age so a responder can discount it | | Correlation presented as causation | Offer competing hypotheses and disconfirming checks | | Hallucinated commands, resources, or explanations | Ground claims in retrieved evidence and prevent unapproved execution | | Permission sprawl | Use least-privilege, scoped credentials and separate read from action authority | | Nondeterministic output | Evaluate repeated runs and constrain critical steps with deterministic policy | | False confidence | Report uncertainty and unresolved evidence, not only a numeric score | | Sensitive operational data | Define retention, model-provider, regional, and audit boundaries before rollout | I do not accept confidence as a substitute for correctness. A 92% label is meaningless if I cannot inspect the evidence, understand the missing context, or see which alternative explanations were tested. I am skeptical of “the agent learns from every incident,” too. Feedback matters only when it changes something the system will use later: a runbook, retrieval source, policy, prompt, regression test, or evaluation set. Whenever I hear the claim, I ask: **where is that learning stored and tested?** For shorter answers to common evaluation questions, see the [AI SRE FAQ](https://rootly.com/ai-sre-guide/frequently-asked-questions). ## Implementation strategies and best practices If I were evaluating an AI SRE system, I would start with a replay—not a live autonomous pilot. Use a representative set of recent incidents and give the system only the information available at the time. Include routine and ambiguous incidents, stale documentation, false leads, and at least one case where the correct action was to wait or escalate. Score the result on operational usefulness: - Did it surface the evidence that mattered? - Did it identify the correct service and owner? - Did it distinguish observation from inference? - Did it preserve plausible competing hypotheses? - Did it recommend a safe next check or action? - Did it know when the evidence was insufficient? - How much human correction was required? ### Adopt a phased maturity model ![Illustration for “Adopt a Phased Maturity Model”](https://rootly.com/assets/ai-sre-guide-the-complete-guide-to-ai-sre-transforming-site-reliability--2.avif) After replay evaluation, move through a narrow sequence: 1. **Observe:** run read-only during live incidents and compare its investigation with the responder’s. 2. **Assist:** let it post evidence and recommendations into the existing incident workflow. 3. **Approve:** enable a small set of actions that always require explicit human approval. 4. **Bound:** permit selected actions only when scope, health checks, rollback, identity, and audit requirements are satisfied. 5. **Expand deliberately:** add incident classes and actions based on evaluated performance, not a general feeling that the agent is “ready.” Start with one service family or incident class where telemetry is strong, ownership is clear, and enough past cases exist to evaluate. Keep the system inside the tools responders already use; a separate interface can add toil instead of removing it. The [implementation guide](https://rootly.com/ai-sre-guide/implementation-guide) turns this into a rollout plan. The [integrations guide](https://rootly.com/ai-sre-guide/integrations) can help teams inventory the data and controls required before a pilot. ## Economic outcomes and reliability metrics I would not justify an AI SRE pilot with a promise to “cut MTTR by X%.” Averages flatten incident severity, hide the spread of results, and can reward quick but risky action. As [“Beyond MTTX”](https://rootly.com/blog/beyond-mttx-a-case-for-qualitative-incident-assessments) argues, time-based metrics need qualitative context. Track a small set of measures across comparable incidents: - time until the responder receives useful, relevant context; - time until the correct service owner is engaged; - percentage of claims linked to inspectable evidence; - percentage of recommendations accepted, modified, or rejected; - unsafe, irrelevant, or overconfident recommendations; - number of manual searches and tool switches required; - responder assessment of whether the system reduced cognitive load; - customer-impact duration and recurrence, interpreted alongside incident severity. For actions, also track rollback success, policy violations, approval behavior, and cases in which the system correctly declined to act. A non-action can be a successful outcome when the alternative is a fast, wrong mitigation. The [metrics and ROI guide](https://rootly.com/ai-sre-guide/metrics-and-roi) offers a more complete evaluation framework. ## Organizational and role impacts This is the claim I am most careful with. AI-assisted incident investigation can help a team handle operational growth without increasing repetitive investigative work at the same rate. That is not the same as proving a company can operate reliably with fewer SREs. The leverage appears when software takes on context collection, evidence organization, record keeping, and well-bounded procedural work. The people still define acceptable risk, set SLOs, design failure modes, improve observability, decide where automation is safe, and turn incident learning into system change. That division matters. Introduce the tool as a headcount replacement and experienced responders have good reason to distrust it. Make a bounded promise—reduce repetitive investigation while preserving human judgment—and the team can test whether it helps. The outcome I want is not fewer humans on the incident channel at any cost. It is fewer humans spending their time reconstructing facts that software could have assembled, and more attention available for decisions only they can own. ## How Rootly AI SRE compares Four kinds of product come up when engineers evaluate AI SRE: an agent built into an observability platform, an agent built into an incident management platform, a standalone AI SRE agent, and an AIOps platform that has added agents. The descriptions below come from each vendor's own public pages, checked on October 14, 2026, and from Rootly's product pages. Names and features in this category change quickly, so check the linked pages before you decide. | Product | How the vendor describes it | Where an investigation starts | What it works from | | --- | --- | --- | --- | | Rootly AI SRE | An investigation and response engine built into Rootly's incident management platform | The moment an alert fires | The tools you connect, plus past incidents in Rootly | | Datadog Bits AI SRE (now Bits Investigation) | An always-on SRE agent | Datadog monitor alerts | Datadog telemetry | | incident.io Investigations | An AI SRE inside incident.io's software reliability platform | When an incident is declared, by condition or on demand | Telemetry, code changes, change events and past incidents | | Resolve AI | An AI SRE platform that goes on-call for you | Alerts, as primary on-call | Your production tools, with context passed to coding agents | | BigPanda | An agentic IT operations platform | Correlated alerts from monitoring tools | Monitoring, service desk, change and past-incident data | ### Rootly AI SRE and Datadog Bits AI SRE Datadog launched its agent as Bits AI SRE and now calls it Bits Investigation. Datadog describes it as ["an always-on SRE agent built to handle complex troubleshooting and late-night alerts"](https://www.datadoghq.com/product/ai/bits-ai-sre/). Its [documentation](https://docs.datadoghq.com/bits_ai/bits_ai_sre/) says investigations start from monitor alerts, work from Datadog telemetry and are billed through Datadog AI credits. **Where it fits:** teams whose metrics, logs, traces and deploy data mostly live in Datadog. The agent sees that data in depth without another integration. **How Rootly differs:** Rootly AI SRE reads across the tools you connect, [Datadog among them](https://rootly.com/integrations), and runs inside the incident: it ranks probable causes with confidence scores and the evidence for each, then stays with responders in Slack or Microsoft Teams through status updates and the retrospective. The two can run together, with Datadog as a data source and Rootly as the place the incident is coordinated. ### Rootly AI SRE and incident.io incident.io describes itself as a [software reliability platform](https://incident.io/) that unifies on-call, agentic root cause analysis, incident response and status pages. Its AI SRE is called Investigations. [incident.io's AI SRE page](https://incident.io/ai-sre) says Investigations starts the moment an incident is declared and connects telemetry, code changes and incident history to name a root cause, "with a confidence score and sources behind every finding." The same page says the only change it can make to your systems is a pull request you review and merge. Its [documentation](https://docs.incident.io/investigations/overview) lets teams run investigations automatically, by condition or on demand, and says each one posts its root cause, evidence and next steps into the incident channel. **Where it fits:** teams that already run on-call and incident response in incident.io and want the agent in the same place. **How Rootly differs:** the two products are close in shape. Each agent runs inside an incident management platform that also handles on-call and status pages, attaches confidence scores and evidence to its findings, and leaves changes to a person. The clearest difference is the starting point. Per [Rootly's AI SRE page](https://rootly.com/ai-sre), Rootly AI SRE starts investigating the moment an alert fires, before your team has finished reading the notification, and it never auto-remediates without human sign-off. Because each agent comes with its platform, teams comparing the two usually decide on the wider incident management platform. ### Rootly AI SRE and Resolve AI Resolve AI calls itself an ["AI SRE platform"](https://resolve.ai/) that "goes on-call on your behalf and autonomously resolves incidents." Its site lists primary on-call that "triages and mitigates alerts" and "escalates to engineers when needed," issue remediation that gives production context to coding agents, and custom SRE agents for production automation. **Where it fits:** teams that want an agent to take first response on alerts and to hand recurring fixes to coding agents. **How Rootly differs:** Rootly AI SRE sits inside the incident workflow responders already run, with on-call, incident channels, status pages and retrospectives in one platform. It recommends fixes with its reasoning shown and, per [Rootly's AI SRE page](https://rootly.com/ai-sre), never auto-remediates without human sign-off. Teams that want an agent to act alone on first response will weigh that differently from teams that want an on-call engineer to approve each action. ### Rootly AI SRE and BigPanda BigPanda presents itself as an [agentic IT operations platform](https://www.bigpanda.io/our-product/). It correlates signals from monitoring tools, and its AI agents "gather insights from service desk activity, past incidents, change data, and external observability sources." Its [AI Incident Assistant, Biggy](https://www.bigpanda.io/our-product/ai-copilot/), adds root-cause analysis to the incident workflow. Depending on which capabilities an admin has enabled, [Biggy also covers parts of the response](https://docs.bigpanda.io/en/manage-incidents-with-ai-incident-assistant): it can open an incident channel, suggest which teams to page, write executive summaries and produce post-mortem reports. **Where it fits:** enterprises with large monitoring estates and IT operations teams, where alert volume and correlation are the first problem to solve. **How Rootly differs:** BigPanda is built around the event stream, and Biggy extends it into the response. Rootly AI SRE is built around the incident. Once an alert pages someone, it investigates across alerts, deploys, code changes and past incidents, then works with responders through coordination, stakeholder updates and the retrospective. If you already run Biggy's incident features, compare the two step by step. If you use BigPanda mainly for correlation, the two run together: AIOps upstream for noise reduction, the AI SRE on the incidents that still reach a person. [AI SRE vs AIOps](https://rootly.com/ai-sre-guide/ai-sre-vs-aiops) covers that split in more detail. ## Conclusion After spending the past year digging into this category, I keep coming back to the same conclusion: the name may be awkward, but the evaluation does not have to be. At the next incident review or product demo, I would ask: 1. What changed? 2. Which source supports each claim? 3. What other hypothesis fits the evidence? 4. What check would disprove the leading hypothesis? 5. What is the smallest reversible next action? 6. Who can approve it, and how is it rolled back? 7. What will the system record if the recommendation is wrong? If the product cannot answer those questions, it may still produce an impressive summary. I would not trust it as an incident investigation partner. AI SRE is most useful when it makes the work visible instead of pretending it has disappeared: evidence before certainty, bounded action before autonomy, and augmentation before replacement. That is narrower than an “autonomous SRE.” It is also a promise an on-call engineer can test, which is where it has to survive. ## Frequently asked questions ### What is an AI SRE agent and what can it do today? An AI SRE agent investigates production incidents alongside the on-call engineer. It gathers alerts, deploys, code changes and past incidents, ranks likely causes with the evidence for each, and recommends the next step within limits a human sets. Rootly AI SRE does this from the moment an alert fires, with confidence scores on each probable cause, similar-incident matching, drafted status updates and generated retrospectives. ### Do AI SRE agents actually work, or are they just hype? They work when they show their evidence and the operational foundations underneath them are sound: usable logs, accurate ownership and maintained runbooks. Judge an agent on the investigation trail it leaves. Treat any product that can't show why it reached a conclusion as unproven. Rootly AI SRE attaches the evidence to every probable cause it ranks, so responders can check the reasoning. ### Is Datadog Bits AI enough, or do we need a dedicated AI SRE? If nearly all of your metrics, logs, traces and deploy data live in Datadog, its agent (Bits AI SRE, now called Bits Investigation) may cover much of the investigation. If the evidence is spread across several tools, an AI SRE that reads across them and works in the incident channel will see more of it. Rootly AI SRE connects to Datadog and your other tools, ranks probable causes with the evidence for each, and works the incident in Slack or Microsoft Teams. ### How is Rootly AI SRE different from incident.io's AI SRE? incident.io's AI SRE, called Investigations, runs inside incident.io's platform and starts when an incident is declared, or by condition or on demand. Rootly AI SRE runs inside Rootly's incident management platform and starts the moment an alert fires. Both attach confidence scores and evidence to their findings and leave changes to a person, and Rootly AI SRE never auto-remediates without human sign-off. ### How is Rootly AI SRE different from Resolve AI? Resolve AI describes itself as an AI SRE platform that goes on-call for you and resolves incidents autonomously. Rootly AI SRE runs inside Rootly's incident management platform, alongside on-call, status pages and retrospectives. It starts investigating when the alert fires, shows its reasoning, and never auto-remediates without human sign-off. ### Do we need an AI SRE if we already use BigPanda? It depends on which BigPanda capabilities you have enabled. BigPanda centers on alert correlation and root-cause analysis, and its AI Incident Assistant, Biggy, can also open incident channels, suggest teams to page, write executive summaries and draft post-mortems. List what your responders still do by hand after the page: checking code changes and deploys, matching past incidents, coordinating responders, updating stakeholders and writing the retrospective. The longer that list, the more an AI SRE such as Rootly adds. Many teams keep AIOps for noise reduction and send what still gets through to the AI SRE. --- # Alert management: a practical guide for engineering teams (2026) URL: https://rootly.com/alert-management Pillar: Alert Management A practical guide to turning telemetry into actionable, owned alerts through better routing, escalation, automation, and AI-assisted triage. When engineers first work with observability, it is natural to focus on collecting more signals: metrics, logs, traces, events, and profiles. Those signals make systems easier to understand, but they also create more conditions that *could* notify someone. A useful alerting system needs a way to decide which conditions require action, who owns that action, and what context they need when the page arrives. That is the role of alert management. It connects observability to on-call and incident response, turning raw signals into a smaller number of actionable, owned interruptions. A good implementation helps responders act quickly without asking them to watch every dashboard or interpret every fluctuation in production. This guide explains that path from first principles. It is for engineers who understand telemetry and monitoring but are beginning to design how alerts should be evaluated, routed, escalated, and improved. ## Key insights - Alert management sits between observability and incident response, turning triggered conditions into owned operational work. - A page should represent an actionable condition, not every unusual signal a monitoring system detects. - Ownership, context, routing, and escalation determine whether an alert reaches someone who can act on it. - Suppression, deduplication, and correlation reduce interruptions only when critical user-facing signals remain protected. - Automation and AI are most useful after the underlying alert path is dependable, observable, and governed by clear controls. ## Explore the alert management guides - **Improve signal quality:** understand [alert fatigue](https://rootly.com/alert-management/alert-fatigue), reduce [alert noise](https://rootly.com/alert-management/alert-noise-reduction), and combine repeated signals through [deduplication and correlation](https://rootly.com/alert-management/alert-deduplication-and-correlation). - **Reach the right responder:** design dependable [alert routing](https://rootly.com/alert-management/alert-routing) and [escalation policies](https://rootly.com/alert-management/alert-escalation-policies). - **Connect and automate the workflow:** evaluate [alert management integrations](https://rootly.com/alert-management/alert-management-integrations), [workflow automation](https://rootly.com/alert-management/alert-management-automation), and [AI-powered alert management](https://rootly.com/alert-management/ai-powered-alert-management). - **Operate and improve the system:** apply [alert management best practices](https://rootly.com/alert-management/alert-management-best-practices) and track [alert management metrics](https://rootly.com/alert-management/alert-management-metrics). - **Evaluate platforms:** learn [how to choose alert management software](https://rootly.com/alert-management/how-to-choose-alert-management-software), then compare the [leading alert management platforms](https://rootly.com/alert-management/best-9-alert-management-software-for-engineering-teams-in-2026). ## What is alert management? Alert management is the process of receiving signals from monitoring and observability tools, deciding which ones require action, adding useful context, and directing that work to the correct responder or automated workflow. At its core, alert management answers a short set of operational questions: - Does this condition require action? - How urgent is it? - Who owns the affected service? - What evidence does the responder need? - Can a safe automation handle it? - What happens if nobody responds? - When should this become an incident? Monitoring and observability help engineers understand a system. Alert management applies operational policy to that information. Incident management coordinates the response after a disruption needs people, communication, decisions, and recovery work. | Layer | Primary job | Typical output | |---|---|---| | Observability and monitoring | Collect and analyze system signals | A dashboard, query result, event, or triggered condition | | Alert management | Evaluate, enrich, group, route, notify, and escalate | An owned alert, ticket, automation, or incident trigger | | Incident management | Coordinate investigation, communication, mitigation, and recovery | A resolved incident with a timeline and follow-up work | This boundary matters because an alert is not automatically an incident. Some alerts are suppressed during maintenance, grouped into an existing problem, handled by automation, or recorded as a ticket for later work. Incidents can also begin without an alert—for example, when a customer or support engineer reports a problem before monitoring detects it. ### Understanding events, alerts, and incidents Events, alerts, and incidents describe different points in the path from system behavior to human response: - **An event** is an observable occurrence, such as a deployment completing, a request timing out, or a database changing leadership. Events can be routine, useful for investigation, or evidence of a problem. - **An alert** is a condition that an operational policy has selected for action. The action might be paging a person, opening a ticket, starting an automation, or adding context to an existing alert. - **An incident** is a disruption that requires a coordinated response. It has an owner, impact, decisions, communication, and a defined resolution process. [OpenTelemetry describes signals](https://opentelemetry.io/docs/concepts/signals/) as system outputs that reveal underlying application and platform activity. Alert management is one of the places where a team turns those signals into decisions. ### The goal of alert management The goal is not to deliver every notification faster. It is to make each interruption worth a responder's attention. A healthy alert-management practice should: - Detect conditions that threaten users or important service behavior - Keep transient and non-actionable activity out of the paging path - Attach ownership, severity, dashboards, runbooks, and recent-change context - Group duplicate or causally related signals - Deliver actionable alerts through a dependable on-call path - Escalate when the first responder is unavailable - Feed what the team learns back into monitors and alert policies Google's discussion of [practical alerting](https://sre.google/sre-book/practical-alerting/) makes a useful distinction: page-worthy conditions go to the on-call rotation, important but subcritical conditions become tickets, and informational data stays available for dashboards. Alert management provides the controls needed to enforce that distinction consistently. ## Why alert management matters Most alerting failures happen after a monitor detects something. The signal exists, but the path from detection to action breaks down. A database latency alert might reach a general engineering channel with no service owner attached. A regional dependency failure might page five downstream teams independently. A responder might receive an error-rate threshold without a dashboard, recent deploy, or runbook. Another alert may go unacknowledged because its escalation policy ends with an unavailable person. These failures create practical costs: - **Slow ownership:** responders spend the first minutes finding the team responsible for the service. - **Duplicate work:** multiple people investigate symptoms of the same underlying failure without realizing it. - **Lost context:** engineers reconstruct deploys, dependencies, and past incidents while the problem is active. - **Missed alerts:** unclear schedules or incomplete escalation paths leave important work unattended. - **Responder fatigue:** repeated low-value pages teach engineers that an alert probably does not matter. - **Inconsistent response:** the outcome depends on who happens to be on call and what they remember. Alert management addresses these gaps by turning the response path into an explicit system. The result should be fewer unnecessary interruptions and more confidence that a page represents work the responder can begin immediately. For the deeper failure modes, see [alert fatigue](https://rootly.com/alert-management/alert-fatigue), [alert noise reduction](https://rootly.com/alert-management/alert-noise-reduction), and [alert deduplication and correlation](https://rootly.com/alert-management/alert-deduplication-and-correlation). ## Where alert management fits within incident management Alert management sits between the systems that observe production and the systems that coordinate people. A simplified flow looks like this: 1. Applications and infrastructure emit telemetry. 2. Observability and monitoring tools evaluate that telemetry. 3. Alert management applies rules for actionability, ownership, severity, grouping, and urgency. 4. On-call systems notify and escalate to the responsible responder. 5. Incident response begins when the disruption needs coordinated investigation and recovery. 6. The incident timeline and [postmortem](https://rootly.com/incident-postmortems) reveal which alert rules, ownership records, and automations should change. The boundary is intentionally permeable. An alert can create an incident automatically when impact is clear, or a responder can promote it after investigation. Resolution data should travel in the other direction as well, closing alerts and giving future correlation or automation better evidence. The [alert-management integrations guide](https://rootly.com/alert-management/alert-management-integrations) explains how this information moves between tools. The [incident-response lifecycle](https://rootly.com/incident-response/lifecycle-process) covers the coordinated work that follows once an incident is declared. ![Flow diagram showing telemetry moving through observability and alert management into suppression, automation, on-call notification, or incident response, with a review loop feeding improvements back into alert policy.](https://rootly.com/assets/alert-management-complete-guide-system-flow.svg) ## How modern alert management works Implementations vary, but a useful end-to-end model has eight stages. ![Eight-stage alert-management lifecycle: ingest; normalize and enrich; suppress, deduplicate, and correlate; prioritize; route; notify and escalate; activate incident response; and review and improve.](https://rootly.com/assets/alert-management-complete-guide-lifecycle.svg) ### 1. Ingest The alert-management system receives triggered conditions from infrastructure monitoring, application performance monitoring, logs, cloud services, synthetic checks, security tools, and other operational sources. ### 2. Normalize and enrich Incoming payloads rarely use the same fields. Normalization establishes a shared representation for service, environment, severity, source, timestamps, and status. Enrichment adds ownership, dependency, dashboard, runbook, recent-deploy, and incident-history context. ### 3. Suppress, deduplicate, and correlate Maintenance windows and known conditions can suppress expected activity. Deduplication removes repeated copies of the same alert. Correlation groups different signals that appear to share a service, dependency, change, or underlying failure. ### 4. Prioritize Urgency should reflect probable user and business impact, not just the size of a metric. This stage determines whether the condition belongs on a dashboard, in a ticket queue, with an automation, or on a responder's pager. ### 5. Route Routing uses service ownership, environment, severity, source, and schedule data to find the responsible team or responder. A fallback path handles missing or stale ownership rather than silently dropping the alert. ### 6. Notify and escalate The system delivers the alert through the appropriate channel and waits for acknowledgement. If the responder is unavailable, an [escalation policy](https://rootly.com/alert-management/alert-escalation-policies) advances to another person, schedule, team, or communication channel. ### 7. Activate incident response When impact requires coordinated work, the alert becomes or joins an incident. Automation can create the incident, open a collaboration channel, assign roles, collect diagnostics, and begin [stakeholder communication](https://rootly.com/incident-communications). ### 8. Review and improve After resolution, teams review which alerts were useful, duplicated, late, misrouted, or missing context. The resulting changes improve thresholds, ownership, runbooks, correlation, and automation. The [automation guide](https://rootly.com/alert-management/alert-management-automation) goes deeper into executable workflows. The integrations guide covers the event flow and reliability requirements between systems. ## Common challenges in alert management The first version of an alerting system usually works for a small service and a small team. Problems become visible as the number of services, owners, tools, and deploys grows. - **Alert fatigue:** repeated non-actionable interruptions reduce attention and increase on-call strain. - **Alert noise:** transient, low-severity, expected, and non-production conditions enter the same path as real service threats. - **Duplicate alerts:** one failure creates many pages or incident records across tools and dependencies. - **Incorrect routing:** ownership data is missing, stale, or expressed differently across systems. - **Escalation gaps:** alerts have no dependable fallback after the first notification fails. - **Low-quality context:** the responder receives a symptom without the evidence needed to investigate it. - **Disconnected tools:** state changes in one system do not propagate to monitoring, chat, tickets, or incident response. - **Rules that never improve:** teams resolve incidents without feeding what they learned back into alert configuration. These are system-design problems. Asking responders to pay closer attention may help briefly, but it does not repair the alert path. ## Core capabilities of modern alert management platforms A platform should support the lifecycle rather than functioning as a notification relay. For someone evaluating the category for the first time, the essential capabilities are: - Reliable ingestion from the team's monitoring and observability tools - A common alert schema with service and environment context - Suppression, maintenance windows, deduplication, and correlation - Ownership-based [alert routing](https://rootly.com/alert-management/alert-routing) - On-call schedules, acknowledgement, and multi-step escalation - Enrichment from service catalogs, deploy systems, dashboards, and runbooks - Automation for incident creation and repeatable first-response work - Bidirectional status updates so resolved work does not remain open elsewhere - Access controls and an audit trail for alert and policy changes - Reporting on alert quality, responder load, routing, and escalation The important question is how these capabilities behave together. Excellent routing cannot compensate for missing ownership data, and automation cannot make a low-quality alert actionable. ## Who is responsible for alert management? Alert management is shared infrastructure with distributed ownership. One team may operate the platform, but alert quality depends on the engineers closest to each service. - **Service owners** decide which conditions require action, supply service context, and maintain the relevant dashboards and runbooks. - **Platform and reliability engineers** establish alerting standards, shared schemas, routing infrastructure, integrations, and safe automation patterns. - **On-call responders** validate whether pages were actionable and identify missing context, incorrect ownership, and noisy conditions. - **Incident commanders** take over coordination when an alert becomes a significant incident and capture decisions that affect future alerting. - **Engineering managers** watch responder load, after-hours interruptions, ownership gaps, and recurring operational work. The review loop matters as much as the initial configuration. Responders see where alert policy meets production reality; the system improves when their findings reach service owners and platform teams. ## The role of AI in modern alert management Alert management already contains a great deal of deterministic automation. Rules can normalize payloads, attach ownership, suppress expected conditions, route by service, and escalate after a timeout. Those behaviors should remain predictable and testable. AI becomes useful where the inputs are less uniform and the decision needs more context. It can: - Summarize a noisy alert and the evidence attached to it - Group signals that are related but do not share an exact fingerprint - Rank alerts using service impact, history, dependencies, and current changes - Retrieve similar incidents and the mitigations that worked previously - Correlate an alert with recent deploys or configuration changes - Suggest investigation steps while showing the evidence behind them ![Eight ways AI can assist alert management: intelligent triage, prioritization, summarization, duplicate detection, event correlation, context enrichment, suggested remediation, and predictive risk.](https://rootly.com/assets/alert-management-complete-guide-ai-capabilities.avif) AI output should not silently become production action. Teams need visibility into the evidence, confidence, and policy behind a recommendation, with approval controls for consequential changes. The [AI-powered alert management guide](https://rootly.com/alert-management/ai-powered-alert-management) covers triage, correlation, and adoption in more detail. The [AI SRE guide](https://rootly.com/ai-sre-guide) explains the broader investigation model: gathering evidence, testing hypotheses, and proposing or executing controlled remediation after an alert fires. ## Characteristics of effective alert management Effective alert management is easier to recognize by its operating behavior than by the number of features configured. - **Alerts are actionable.** A responder can see the affected service, observed symptom, urgency, evidence, owner, and useful next step. - **Urgency follows impact.** Paging policy reflects user and business risk rather than treating every technical threshold equally. - **Ownership is explicit.** Every production service has a maintained team and fallback path. - **Automation is observable.** Engineers can see what ran, why it ran, what changed, and how to stop or reverse it. - **Critical signals remain protected.** Suppression and correlation reduce noise without hiding important user-facing symptoms. - **The system learns.** Incidents and responder feedback lead to changes in alerts, ownership, runbooks, and workflows. ## Key metrics every engineering team should track Alert-management metrics should reveal signal quality and responder load. They should help a team decide which alert or service to investigate next—not simply produce a faster-looking aggregate. - **Total alerts by service:** shows where operational work is concentrated. - **Alerts per on-call shift:** reveals how much interruption a responder actually experiences. - **After-hours alert percentage:** distinguishes ordinary operational work from sleep-disrupting pages. - **Actionable-alert rate:** measures how often an alert leads to a useful investigation or action. - **Duplicate-alert rate:** tracks repeated copies of the same underlying condition. - **Alert compression rate:** measures how many raw signals become grouped, actionable alert instances. - **Unacknowledged-alert rate:** reveals delivery, schedule, ownership, or escalation failures. - **Escalation rate:** shows how often the first notification path fails to reach an available owner. - **Alert-to-incident ratio:** helps distinguish routine alert work from conditions requiring coordinated response. - **Repeat-alert rate:** identifies alerts that return without a lasting fix or useful policy change. There is no universal healthy number for every metric. Establish a baseline, segment it by service and severity, and look for sustained improvement without a rise in missed incidents. The [alert-management metrics guide](https://rootly.com/alert-management/alert-management-metrics) provides the formulas and review framework. ## Choosing alert management software If you are new to the category, begin with the path an alert follows today. List the monitoring sources, ownership records, schedules, notification channels, escalation rules, incident tools, and review process. The gaps in that map are more useful than a generic feature checklist. ### Decide what kind of system you need Monitoring-native alerting may be enough for a small team with a limited number of services. Dedicated on-call tooling adds schedules, acknowledgement, and escalation. A full-lifecycle incident-management platform connects that alert path to incident coordination, communication, automation, and post-incident learning. ### Test the fundamentals first Before evaluating advanced capabilities, verify that the platform can ingest your real sources, normalize their fields, find the current service owner, group duplicates, route by impact, and escalate reliably. Test missing ownership and integration failures as deliberately as the happy path. ### Use real alert traffic in the evaluation A clean demonstration does not reveal how a platform handles flapping monitors, inconsistent service names, duplicate signals, stale schedules, or a burst during a dependency failure. A useful proof of concept includes representative noisy sources and the responders who will receive them. ### Evaluate automation and AI separately Deterministic workflows should be predictable, testable, and auditable. AI features should show their evidence, confidence, limits, and approval model. A compelling summary does not make up for unreliable routing or escalation. ### Check how the platform improves over time Teams need reporting that connects alerts to owners, services, incidents, and responder load. They should be able to identify noisy rules, recurring failures, missing runbooks, and alerts that never produce action. For a full scorecard and proof-of-concept plan, see [how to choose alert management software](https://rootly.com/alert-management/how-to-choose-alert-management-software). The [software comparison](https://rootly.com/alert-management/best-9-alert-management-software-for-engineering-teams-in-2026) covers the current market separately. ## Build an alert path engineers can trust Collecting telemetry is the beginning of operational awareness. Alert management turns that awareness into a dependable response path: decide what deserves action, add the context needed to start, find the owner, escalate when necessary, and learn from the result. Start with one production service and map the path from a triggered condition to resolution. Remove an alert that never leads to action. Add ownership and a runbook to one that does. Test what happens when the primary responder is unavailable. Those small changes create a system engineers can trust before more sophisticated automation or AI enters the picture. ## Frequently asked questions ### What is the difference between alert management and incident management? Alert management evaluates signals, adds context, groups related alerts, and routes actionable work to the right responder. Incident management begins when a disruption requires coordinated investigation, communication, decisions, and recovery. An alert may trigger an incident, but many alerts are suppressed, automated, ticketed, or resolved without one. ### How do I know when my team has outgrown basic alerting tools? Common signs include alerts going to broad channels, responders manually finding service owners, duplicate pages for one failure, missing escalation paths, and no reliable way to measure alert quality or responder load. ### How can a team reduce false-positive alerts without missing incidents? Start by measuring which alerts lead to action. Tune thresholds around user impact, require conditions to persist long enough to exclude transient failures, suppress expected maintenance, and correlate related signals. Keep critical customer-facing symptoms outside aggressive suppression rules. ### What should every production alert include? Include the affected service and environment, observed symptom, current value or evidence, severity or urgency, owner, relevant dashboard and runbook links, recent changes, and the condition that will resolve the alert. ### How often should alert rules be reviewed? Review alerts after incidents and whenever service ownership, architecture, traffic patterns, or deployment behavior changes. A recurring review is useful, but operational changes should trigger review sooner than a fixed calendar date. --- # Incident response: a complete guide to effective crisis management URL: https://rootly.com/incident-response Pillar: Incident Response Incident response works best as a practiced system with clear roles, shared priorities, reliable communication, and a process that improves after every disruption. When an unexpected disruption strikes, the ability of an organization to respond quickly and effectively can determine whether it regains control or suffers lasting consequences. Incident response is the structured process that helps teams contain threats, minimize downtime, and restore systems to normal operation. It is more than a set of technical steps. A strong incident response plan builds resilience, protects customer trust, and reduces business risk by ensuring that every event is handled with clarity and consistency. For professionals working in reliability, security, and IT, incident response is not optional. It is a core discipline that connects strategy with action. An established incident response lifecycle, supported by a trained incident response team and tested incident response playbooks, provides the framework needed to face uncertainty with confidence. By adopting best practices in incident response, measuring progress with metrics such as MTTR, and leveraging modern tools, organizations create a culture of preparedness. The result is stronger operational resilience and better alignment with related practices such as incident management and problem management. ## Key takeaways - Incident response replaces improvisation with a shared way to detect, coordinate, resolve, recover, and learn. - Preparation matters most before the alert: define roles, severity, communication paths, and practiced procedures while the stakes are low. - During an incident, one clear command structure keeps decisions, technical work, and stakeholder updates moving from the same facts. - Recovery is not the finish line; reviews, metrics, and follow-up work are how the next response gets easier. ## What is incident response? Incident response is the structured approach that organizations use to handle unexpected events such as security breaches, outages, or critical system failures. It provides a framework for detecting, containing, investigating, and resolving incidents in a way that minimizes disruption and restores services as quickly as possible. Rather than leaving teams to improvise under pressure, incident response brings order and consistency to the process of managing incidents. The importance of incident response can be seen in the outcomes it delivers. A well designed incident response plan helps teams: - Reduce downtime and maintain business continuity - Limit financial and reputational damage - Protect sensitive information and meet compliance requirements - Learn from root causes to prevent repeat incidents - Build stronger resilience across systems and teams Why incident response matters goes beyond immediate recovery. It creates a culture of preparedness, ensuring that teams remain calm and coordinated even when facing high stress situations. A dedicated incident response team, supported by playbooks and training, provides the structure needed to act decisively when disruption occurs. While incident response centers on the tactical steps of addressing and resolving an incident, incident management takes a broader perspective by overseeing the full process of restoring services and maintaining continuity across the organization. ## Incident response lifecycle ![Illustration for “Incident Response Lifecycle”](https://rootly.com/assets/incident-response-incident-response-a-complete-guide-to-effective-crisis.avif) Every effective incident response plan follows a lifecycle that turns moments of chaos into structured, coordinated action. By breaking incidents into clear stages, teams can move from the first alert to long-term improvements without missing critical steps. This lifecycle ensures that responses are consistent, measurable, and repeatable regardless of the size or severity of the incident. ### Step 1: preparation Preparation lays the foundation for successful response. Teams establish [severity levels](https://rootly.com/incident-response/support-levels), maintain updated incident response playbooks, and run training exercises to ensure that everyone knows their role before an incident occurs. Strong preparation reduces hesitation and creates the muscle memory needed to act decisively when systems fail. ### Step 2: detection and alerting Most incidents begin with an alert from monitoring tools or a report from users. At this stage, the focus is on validating whether the event is truly an incident, assessing its scope, and escalating if necessary. Effective detection minimizes wasted time and reduces the impact window. ### Step 3: triage and prioritization Not all incidents carry the same weight. During triage, teams classify events by severity and potential business impact, ensuring that [high-risk issues](https://rootly.com/blog/mastering-sev0-best-practices-for-handling-high-severity-incidents) receive immediate attention while less critical problems are handled in order. Prioritization prevents teams from being overwhelmed and directs resources where they matter most. ### Step 4: containment Containment strategies aim to limit the blast radius of the incident. This may include rolling back changes, rerouting traffic, or disabling a failing service. Containment rarely resolves the root cause but buys time to protect customers and maintain stability while deeper investigation continues. ### Step 5: resolution and eradication In this stage, technical responders diagnose and fix the underlying issue. Whether it involves infrastructure repairs, code changes, or configuration updates, the goal is to eliminate the root cause and restore full functionality. Resolution is about addressing the real problem, not just applying temporary patches. ### Step 6: recovery Recovery involves carefully bringing systems back online while monitoring for regression. Teams restore services gradually, validate performance, and ensure that normal operations resume without cascading failures. Transparent communication with stakeholders during this stage is just as important as the technical work itself. ### Step 7: post-incident review Once the incident is resolved, the focus shifts to learning. A [blameless postmortem](https://rootly.com/incident-postmortems/blameless) reviews what happened, why decisions were made, and how processes can improve. Metrics such as MTTR help measure progress over time, while documenting lessons learned ensures the organization grows stronger with every incident. The incident response lifecycle creates predictability under pressure. By following each step with discipline, teams reduce downtime, protect customer trust, and transform disruptions into opportunities for improvement. ## [Incident response Teams & roles](https://rootly.com/incident-response/team-roles) ![Illustration for “Incident Response Teams & Roles”](https://rootly.com/assets/incident-response-incident-response-a-complete-guide-to-effective-crisis-2.avif) Even the best defined lifecycle cannot succeed without the right people in place. An incident response team provides the structure and accountability that turn playbooks into real-world action. When roles are unclear, teams risk duplicating efforts, overlooking key steps, or leaving leadership without the information needed to make decisions. A strong team is built on clearly defined roles and responsibilities: - **Incident Commander** – Directs the overall response, sets priorities, and ensures actions remain aligned with business goals. - **Technical Responders** – Engineers or specialists who investigate the issue, apply fixes, and collaborate on long-term solutions. - **Communications Lead** – Manages information flow to executives, stakeholders, and customers, ensuring consistent and timely updates. - **Scribe or Documenter** – Records incident details, key decisions, and timelines to create an accurate post-incident record. - **Executive Liaison** – Connects business leadership with the technical response, balancing risk considerations and organizational priorities. Together, these roles ensure that incidents are handled with speed and clarity. The Incident Commander provides a single point of authority, responders focus on resolution, and communication remains consistent across the organization. This structure prevents confusion during high pressure moments and shortens recovery times. ## Incident response playbooks and runbooks When an incident strikes, even experienced teams can lose valuable minutes deciding what to do next. This is where playbooks and runbooks come in. **Playbooks** outline the strategy for handling common types of incidents. They provide guidance on steps to take, who to involve, and how to make decisions. For example, a playbook for a DDoS attack might include escalation thresholds, containment strategies, and communication guidelines. **Runbooks** take this a step further by offering detailed, step by step instructions for executing specific tasks. Think of them as checklists or scripts that responders can follow in real time, such as how to roll back a failed deployment or isolate a compromised server. By creating and practising these resources ahead of time, your team reduces hesitation, acts with consistency, and avoids reinventing the wheel during critical moments. ## [AI and automation in incident response](https://rootly.com/blog/ai-in-incident-response-how-automation-improves-mttr) ![Illustration for “AI and Automation in Incident Response”](https://rootly.com/assets/incident-response-incident-response-a-complete-guide-to-effective-crisis-3.avif) The incident response lifecycle and supporting playbooks create a structured foundation for handling disruptions. Yet in modern environments, the scale and speed of incidents often exceed what humans can manage alone. This is where AI and automation strengthen response by reducing manual toil, filtering noise, and accelerating recovery. [AI-driven detection systems](https://rootly.com/blog/ai-driven-incident-response-for-sres-best-practices-use-cases-risks-and-mttr-reduction) analyze patterns across logs, metrics, and telemetry to spot anomalies earlier than traditional monitoring. [Automation](https://rootly.com/incident-response/automation) then executes predefined remediation steps, from restarting failed services to rolling back faulty deployments, without waiting for manual intervention. Together, these capabilities shorten **Mean Time to Resolution (MTTR)** and prevent small issues from growing into widespread outages. Beyond speed, automation improves consistency. A well designed automated workflow ensures the same containment and recovery steps are applied every time, reducing human error during high stress moments. It also frees responders to focus on higher value analysis rather than repetitive manual actions. Over time, AI tools that integrate with incident response playbooks and runbooks evolve into self-healing systems that both resolve incidents and prevent future ones. ## Best practices and metrics Clear roles and workflows form the backbone of an effective team, but discipline alone is not enough. For incident response to scale reliably, organizations need a set of best practices that guide decision-making across incidents of every type and severity. These practices ensure that the structure you put in place is applied consistently under pressure and that responders have the confidence to act decisively, even in ambiguous or fast-changing situations. ### Core best practices - **Preparedness as a continuous investment:** Training sessions, tabletop simulations, and game days build muscle memory so responders can act decisively in high-stress moments. Preparation is not a one-time activity; it should be integrated into quarterly and annual planning so that readiness keeps pace with evolving systems and threats. - **Communication with intent:** Updates must flow through a single, trusted channel. This prevents misinformation and builds trust with executives, customers, and partners. Mature teams often establish pre-approved templates for [status updates](https://rootly.com/blog/status-pages-101-how-to-create-a-status-page-you-and-your-customers-will-actually-want-to-use), ensuring speed without sacrificing clarity. - **Blameless postmortems:** Instead of focusing on who caused a failure, mature teams ask what conditions allowed it to happen. This approach fuels learning, encourages transparency, and helps surface systemic weaknesses that would otherwise remain hidden. - **Living documentation:** Playbooks, runbooks, and knowledge bases should evolve after every incident. Teams that update documentation in real time avoid the common pitfall of relying on outdated guides that no longer reflect current infrastructure. - **Automation where it accelerates recovery:** Automated workflows reduce toil and human error, but oversight remains crucial when business-critical systems are at stake. Best-in-class programs balance automation with human judgment, using escalation triggers to ensure critical decisions still involve experts. ### Expanding beyond the basics Best practices also extend to how organizations integrate incident response into their broader culture and governance: - **Executive sponsorship:** Leadership buy-in ensures that incident response is recognized not just as an engineering function but as a business-critical capability. - **Cross-team collaboration:** Security, reliability, and compliance functions should all participate in incident reviews to prevent silos and ensure holistic improvements. - **Continuous improvement cycles:** Mature organizations schedule follow-ups to confirm that action items from postmortems are completed and measurable outcomes are tracked. ### Outcome-driven metrics Practices alone are not enough. You also need to measure whether they deliver meaningful results. Many organizations get distracted by vanity metrics that look impressive on dashboards but reveal little about real reliability. The strongest programs focus instead on metrics that directly reflect resilience, operational efficiency, and customer trust: - **Mean Time to Detect (MTTD):** Measures how quickly incidents are discovered, setting the pace for the entire response. A lower MTTD reduces the window of impact and gives teams more time to act before users notice issues. Improving this metric often requires tuning alert thresholds and investing in smarter anomaly detection. - [**Mean Time to Resolve (MTTR)**](https://rootly.com/blog/5-proven-strategies-to-reduce-mttr)**:** The most critical benchmark of resilience, tracking how long it takes to restore full service after disruption. Faster resolution means less customer frustration and reduced revenue loss during downtime. Teams often lower MTTR through playbooks, automation, and streamlined communication channels. - **Mean Time Between Failures (MTBF):** Highlights the durability of systems by showing how frequently failures occur. A higher MTBF indicates stronger architecture and fewer service interruptions over time. It also helps leaders assess whether resilience investments are truly reducing incident frequency. - **SLA and SLO compliance:** Demonstrates whether reliability commitments and error budgets are being upheld. Consistent compliance builds trust with customers and keeps teams accountable to clear performance targets. Regular reviews of SLA and SLO data also guide engineering priorities and resource allocation. - **System uptime and customer trust signals:** The ultimate indicators of whether users experience consistent, dependable service. Uptime paired with trust signals like churn, NPS, or support volumes paints a complete picture of resilience. Even during outages, transparent communication can preserve customer trust and loyalty. ### Moving beyond vanity metrics Metrics should not only quantify speed but also measure quality and customer impact. Organizations sometimes celebrate a short MTTR while ignoring whether the fix was sustainable or if customers experienced degraded performance after resolution. To avoid this trap, leading teams pair technical metrics with **business-level indicators** such as churn, NPS, or transaction success rates during and after incidents. ### Benchmarking and continuous review Finally, metrics become most valuable when tracked over time and compared against internal goals or industry benchmarks. By reviewing trends quarterly, leaders can see whether their investments in tooling, automation, or training are actually reducing downtime and improving resilience. Over time, this transforms metrics from static numbers into a feedback loop that strengthens both technology and organizational culture. By centering on these outcome-driven metrics and embedding best practices into everyday operations, incident response remains grounded in what matters most: reducing downtime, protecting revenue, and strengthening confidence in your systems. ## Incident response vs related practices Measuring performance with the right metrics shows how effective incident response can be, but it is only one piece of a larger reliability ecosystem. To see its full value, it helps to place incident response alongside neighboring practices that share similar goals yet differ in scope and focus. ### [Incident response vs incident management](https://rootly.com/blog/incident-management-vs-incident-response-key-differences-best-practices) Incident response deals with the tactical actions required to detect, contain, and resolve disruptions. It addresses immediate questions such as _What is failing? Who is responding? How do we restore service quickly?_ Incident management takes a wider view by coordinating communication, resources, and recovery at the organizational level. In most programs, incident response is part of incident management, providing the technical execution that management aligns with continuity and business priorities. ### Incident response vs problem management Problem management looks beyond individual events. Its aim is to uncover root causes, study recurring failures, and prevent the same issues from happening again. Incident response restores service in the moment, while problem management builds long-term resilience by addressing underlying weaknesses. The two are complementary, creating a cycle of rapid recovery and steady improvement. Clarifying these distinctions prevents overlap, avoids wasted effort, and ensures every team knows its role. It also provides leaders with the framework to balance fast tactical recovery with proactive prevention. ## Tools for incident response Technology is what turns good practices into reliable execution. Even with clear roles and a tested lifecycle, teams need the right tools to detect issues early, coordinate quickly, and recover with confidence. Incident response platforms amplify human decision-making, streamline workflows, and shorten recovery times by weaving monitoring, communication, and automation into a connected system. ### Common categories of incident response tools include: - **Monitoring and alerting:** Platforms such as Prometheus, Datadog, and New Relic provide visibility into system health and generate actionable alerts. - **Incident management and orchestration:** Tools like [PagerDuty](https://rootly.com/blog/pagerduty-pricing-is-it-worth-the-high-cost-in-2024), Opsgenie, Squadcast, and Rootly help coordinate on-call rotations, manage escalations, and automate workflows. - **Collaboration and communication:** Slack, Microsoft Teams, and Zoom act as war rooms where updates are centralized and stakeholders remain aligned. - **Post-incident analysis and tracking:** Jira, Confluence, and Statuspage capture timelines, measure MTTR, and ensure incidents translate into long-term improvements. The best programs focus less on the number of tools and more on how they work together. Some teams combine observability platforms with orchestration tools like PagerDuty or Opsgenie. Others streamline their process directly in Slack. At [Rootly](https://rootly.com/), we help teams do this by automating workflows and capturing learnings automatically, but every organization should choose the stack that best fits its size, systems, and reliability goals. Editor’s note: Atlassian is retiring Opsgenie, with end-of-life scheduled for April 2027—factor a migration path into any evaluation. --- # On-call software: what engineering teams should look for URL: https://rootly.com/on-call-software Pillar: On-Call Operations What to look for in on-call software: who owns an alert, who gets paged, and what happens when nobody answers the first page. At 2:13 a.m., monitoring detects a sharp increase in checkout failures. Detection is only the beginning. Something still has to decide which team owns the service, who is covering it tonight, how to reach that person, what context to send, and when to escalate if nobody responds. That is the job of on-call software. As a solutions engineer, I spend a lot of time looking at the path between an alert and the engineer expected to act on it. The schedule is the most visible part, but it is rarely where the hardest problems are. The real test is whether the system can turn an operational signal into accountable human action, including when ownership is stale, a notification channel fails, or fifty related alerts arrive at once. This guide explains how on-call software works, which capabilities matter, how it differs from adjacent reliability tools, and what engineering teams should test before choosing a platform. ## Key takeaways - On-call software manages the people and policies responsible for operational alerts: schedules, routing, notification delivery, acknowledgments, and escalation. - Monitoring detects a condition. On-call software determines who needs to know and what happens if that person cannot respond. - The most important capabilities are reliable delivery, flexible scheduling, deterministic escalation, actionable context, noise controls, integrations, and auditability. - A product demo shows the happy path. A useful evaluation tests missed acknowledgments, alert storms, stale ownership, schedule overrides, and failed notification channels. - Software can enforce an on-call model, but it cannot repair noisy alerts, missing ownership, inadequate staffing, or responders who lack the authority to mitigate. ## What is on-call software? On-call software manages the engineering teams responsible for responding to operational alerts outside and inside normal working hours. It maintains schedules and rotations, resolves the current responder, delivers notifications, records acknowledgments, and executes escalation policies when the first person does not respond. SRE, platform, infrastructure, DevOps, security, and product engineering teams use it to answer five practical questions: 1. Who owns this service or failure mode? 2. Who is responsible for it right now? 3. How should that person be reached? 4. What do they need in order to act? 5. What happens if they cannot respond? The software owns the consistent execution of those policies. The engineering organization still owns the policies themselves: service ownership, coverage expectations, alert quality, escalation authority, and the conditions under which a page should become a coordinated incident. That distinction matters. A platform can deliver an alert exactly as configured while the wider on-call system still fails. If the service points to the wrong team or the notification contains no useful context, faster delivery only gets confusion to the responder sooner. ## How does on-call software work? ![Diagram showing how monitoring signals move through alert grouping, ownership, scheduling, notification, acknowledgment, escalation, and incident response](https://rootly.com/assets/on-call-software-signal-to-accountable-response.svg) The basic path begins upstream in monitoring and observability: 1. **A signal crosses a condition.** A monitoring system detects an error-rate increase, failed health check, security event, exhausted resource, or another condition that may require action. 2. **The alert is normalized and grouped.** Alert-management rules deduplicate related events, suppress known noise, attach severity, and determine whether an interruption is warranted. 3. **Ownership and policy are resolved.** The platform maps the alert to a service, team, environment, or routing rule, then finds the active schedule and escalation policy. 4. **The responder is notified.** The system attempts push, phone, SMS, email, chat, or another configured channel and records delivery. 5. **The responder acknowledges or the policy escalates.** An acknowledgment stops the current escalation path. No response moves the alert to a backup, another team, or a management escalation according to explicit rules. 6. **The responder investigates or declares an incident.** The page should carry enough context to begin triage. If the impact requires broader coordination, the workflow moves into [incident response](https://rootly.com/incident-response/lifecycle-process). The middle of this path is where on-call software does its most important work. It resolves responsibility at a specific moment and keeps executing the policy until someone accountable responds. ## Core capabilities of on-call management software ### Schedules, rotations, and overrides [On-call scheduling](https://rootly.com/on-call-software/schedules-and-rotations) establishes who is responsible during a particular window. A useful platform should support primary and secondary rotations, schedule layers, time zones, holidays, temporary overrides, shift swaps, and follow-the-sun coverage without making the resulting schedule impossible to inspect. Look beyond whether a calendar can be created. Engineers need to know who is actually on call after overrides are applied, whether any gaps exist, and how a change affects downstream escalation. The person shown in the final schedule—not the base rotation—is the person the platform must page. ### Alert routing Routing connects an alert to the correct service, team, schedule, and policy. Rules may use the alert source, service, environment, severity, region, or metadata supplied by a service catalog. Good routing reduces the number of humans an alert has to pass through before it reaches someone able to act. Ganesh Datta, co-founder and CTO at Cortex, [describes the familiar 2:00 a.m. problem](https://rootly.com/humans-of-reliability/ganesh-datta-cortex): an engineer gets paged but cannot tell who owns the affected service or what it does. On-call software should consume reliable ownership data, not become another place where teams maintain a conflicting copy. ### Notification delivery and acknowledgment A notification has to arrive through channels responders will notice under real conditions. Push is convenient, but it should not be the only delivery path for urgent pages. Phone and SMS fallbacks, delivery status, channel-specific retries, and acknowledgment controls all matter. Reliable delivery also means distinguishing between “sent,” “delivered,” and “acknowledged.” Those are different operational states. If a push provider accepts a message but the responder never sees it, the escalation policy still needs to move. ### Escalation policies [Escalation policies](https://rootly.com/on-call-software/escalation-policies) define what happens when the first responder cannot acknowledge or needs additional expertise. They can move from a primary to a backup, engage another service team, or bring in an incident commander. The best policies are explicit enough to remove guesswork and simple enough to reason about while tired. Deep trees of conditional rules may look flexible in a configuration screen but become difficult to test and maintain. The platform should make the active path and its timing visible. ### Alert grouping and noise reduction An on-call platform should help prevent one failure from producing dozens of independent interruptions. Deduplication, grouping, suppression, maintenance windows, rate limits, and routing rules can reduce unnecessary pages without hiding distinct impact. Noise control is not the same as muting alerts. It should preserve the information needed to understand scope while reducing repeated interruptions. The dedicated [alert fatigue guide](https://rootly.com/on-call-software/alert-fatigue) covers the operational work behind that distinction. ### Operational context The notification is the responder's starting point. It should identify the affected service and environment, summarize the triggering condition, show severity and customer impact when known, and link to the relevant dashboards, logs, traces, deploys, runbooks, owners, and recent incidents. Gandhi Mathi Nathan Kumar, Principal Incident Commander at Twilio, [measures responder friction in clicks and minutes](https://rootly.com/humans-of-reliability/gandhi-kumar-twilio). Every avoidable lookup delays orientation. An on-call platform does not need to reproduce the observability stack, but it should bring the first useful evidence into the page. ### Integrations and workflow handoff On-call software sits between systems. It needs dependable integrations with monitoring and observability tools, service catalogs, chat, ticketing, incident management, and status pages. APIs, webhooks, and infrastructure-as-code support matter when teams need routing and escalation configuration to follow the same review process as other production changes. The handoff into incident response should also be clear. An acknowledgment says someone has taken responsibility for the alert. It does not necessarily mean the impact is understood, mitigated, or communicated. ### Reporting and auditability The platform should record alert receipt, routing decisions, delivery attempts, acknowledgments, overrides, escalations, and configuration changes. That history helps a team reconstruct what happened and distinguish a slow response from a failed policy. Useful [on-call metrics](https://rootly.com/on-call-software/metrics) include actionable pages per shift, off-hours pages, escalation frequency, alert volume by service, repeated pages for the same condition, uncovered time, overrides, and the concentration of load across responders. Averages alone can hide the person or service absorbing most of the burden. ### Security and administration On-call data contains phone numbers, schedules, incident details, and sometimes sensitive operational context. Evaluate role-based access, SSO, audit logs, data retention, regional requirements, and how integrations are authorized. Configuration changes deserve particular care. A modified routing rule or deleted schedule can prevent the right person from being reached. Teams should be able to review who changed a policy, when it changed, and what the previous state was. ## What should engineering teams look for in on-call management software? Look for reliable delivery, flexible scheduling, deterministic escalation, actionable alert context, strong integrations, effective noise controls, and enough auditability to understand both response performance and responder load. That is the short answer. During an evaluation, I would turn each capability into a failure the platform has to handle: | Evaluate | Questions to ask | | --- | --- | | Delivery reliability | What happens when push delivery fails? Are phone and SMS independent fallbacks? Can the team see each delivery state? | | Scheduling | Can responders swap shifts or add an override without breaking escalation, reporting, or follow-the-sun coverage? | | Escalation | Can policies represent primary, backup, cross-team, and management escalation without becoming opaque? | | Alert quality | Can related alerts be grouped without suppressing distinct failures or customer impact? | | Operational context | Can a responder identify the affected service, impact, owner, and first investigation links from the page? | | Integrations | Does the platform work with the team's actual monitoring, observability, service catalog, chat, and ticketing systems? | | Configuration management | Can schedules, routing, and policies be reviewed, audited, exported, or managed as code? | | Analytics | Can the team find noisy services, ineffective escalations, coverage gaps, and responders carrying disproportionate load? | | Security | Are access, sensitive contact data, audit history, and integration credentials governed appropriately? | | Platform resilience | How does paging behave during a provider, regional, push, telephony, or chat outage? | | Migration | Can users, schedules, policies, integrations, and historical requirements be validated before cutover? | Feature availability is only the first question. The implementation and failure behavior determine whether a capability is safe to depend on. ## Test the failure path, not just the happy path Most on-call demos follow an ideal sequence: an alert arrives, the correct person receives it, and they acknowledge immediately. Production is more creative. Before choosing a platform, run a small operational exercise: - Silence or disconnect the primary responder's phone and observe the fallback. - Add a last-minute override and confirm every relevant policy uses it. - Send a burst of related alerts and inspect grouping, rate limits, and context. - Route an alert with missing or stale service ownership. - Disable one notification channel. - Escalate from one service team to a dependency owner. - Test a handoff between time zones. - Deny an integration permission and inspect the failure. - Reconstruct the full response from the audit history. These tests show whether the system fails visibly and predictably. They also reveal how much operational work hides behind a polished interface. If the team is migrating from another platform, test both systems against the same fixtures. Rootly co-founder Andre Yang's [on-call migration guide](https://rootly.com/blog/what-a-good-on-call-migration-looks-like) explains why schedules alone are the easy part; users, policies, integrations, alert flow, and edge cases all have to move together. ## On-call software versus adjacent reliability tools The categories overlap because many platforms combine them. It is still useful to separate the responsibilities: | System | Primary responsibility | | --- | --- | | Monitoring and observability | Detect system behavior and support investigation | | Alert management | Turn signals into actionable alerts and reduce noise | | On-call software | Determine who responds and execute notification and escalation policies | | Incident management | Coordinate investigation, mitigation, communication, and learning after an incident is declared | | Service catalog | Record ownership, dependencies, and operational metadata | | Status page | Communicate service impact to internal or external audiences | An on-call platform may provide alert grouping, incident workflows, or service ownership features. That does not erase the boundaries. During procurement, map each required workflow to the system that will be authoritative for it. Otherwise, teams end up with different owners, severities, and service names in every tool. ## Match the software to the engineering team There is no universally correct on-call configuration. The system should fit the organization's scale, architecture, risk, and staffing model. - **Small engineering teams** may prioritize simple schedules, quick setup, dependable delivery, and policies that do not require a full-time administrator. - **Microservices organizations** need strong service ownership, metadata-based routing, dependency context, and configuration automation. - **Distributed teams** need reliable time-zone handling, follow-the-sun rotations, explicit handoffs, and visibility into local holidays and overrides. The [global on-call guide](https://rootly.com/blog/distributed-and-global-on-call-best-practices-for-24-7-teams) explores those tradeoffs. - **Regulated organizations** may place more weight on permissions, audit history, data location, retention, and separation of duties. - **High-volume teams** need serious grouping, deduplication, suppression, and analytics so scale does not translate directly into interruptions. The expected human load matters as much as the architecture. [On-call compensation](https://rootly.com/on-call-software/pay), recovery time, rotation size, and backup coverage shape whether the program remains sustainable. Adriana Villela, Principal DevRel at Dynatrace, [connects on-call stress with responder effectiveness](https://rootly.com/humans-of-reliability/adriana-villela). A scheduling feature can distribute shifts; it cannot decide whether the burden is fair. ## Implementing or migrating on-call software Treat the rollout as an operational change, not a calendar import. 1. **Inventory services and owners.** Resolve gaps before they become routing rules. 2. **Audit alerts.** Decide which conditions require immediate human action and remove stale integrations. 3. **Define coverage.** Document primary, backup, cross-team, and management escalation. 4. **Build schedules.** Include overrides, holidays, time zones, and expected recovery. 5. **Attach context.** Connect runbooks, dashboards, deploy information, ownership, and incident workflows. 6. **Configure delivery.** Verify channels, fallbacks, acknowledgment behavior, and contact data. 7. **Test failure paths.** Exercise the scenarios above with representative teams. 8. **Pilot before expanding.** Start with a bounded service group and incorporate responder feedback. 9. **Plan cutover and rollback.** Avoid an ambiguous period in which both systems appear authoritative. 10. **Review the first rotations.** Inspect alert quality, missed pages, escalations, overrides, and human load. The [culture and training guide](https://rootly.com/on-call-software/culture-training) covers shadowing, readiness, and debriefs for responders joining a rotation. The [automation guide](https://rootly.com/on-call-software/automation) addresses bounded automation after the basic response path is dependable. ## What on-call software cannot fix On-call software makes an operating model executable and visible. It cannot compensate for: - Alerts that do not require immediate action - Services without a current owner - Rotations too small to provide recovery time - Backups who are unprepared or unavailable - Responders without permission or authority to mitigate - Runbooks that are missing, stale, or untested - A culture that treats every page as an individual failure Those conditions often surface as tool complaints because the page is where engineers experience them. Replacing the platform without changing the underlying condition moves the same failure into a new interface. A dependable system makes these problems easier to see. It shows coverage gaps, noisy services, repeated escalations, stale policies, and uneven responder load. The engineering organization still has to act on that evidence. ## The standard I use The right on-call software reliably carries an actionable signal to an accountable responder. It makes the current owner clear, brings enough context to begin, escalates predictably, and leaves an audit trail when something goes wrong. I would not choose a platform solely because it can page someone quickly. I would choose it based on whether the team can understand and trust the entire path—from the first signal to the person taking responsibility—especially when the happy path breaks. ## Frequently asked questions ### What is on-call software? On-call software manages the people and policies responsible for responding to operational alerts. It maintains schedules and rotations, works out who is responsible right now, delivers notifications, records acknowledgments, and escalates when the first responder does not answer. ### How is on-call software different from monitoring? Monitoring detects a condition and supports the investigation. On-call software decides who needs to know about that condition, reaches them through the configured channels, and runs the escalation policy if they cannot respond. ### What features matter most in on-call software? Reliable notification delivery with phone and SMS fallbacks, schedules that handle overrides and time zones, explicit escalation policies, alert grouping and noise controls, useful context in the page itself, integrations with monitoring, chat and ticketing tools, and an audit trail of every routing decision and configuration change. ### How should engineering teams evaluate on-call software? Run failure scenarios as well as the demo path. Silence the primary responder's phone, add a last-minute override, send a burst of related alerts, route an alert with stale ownership, disable a notification channel, then reconstruct the whole response from the audit history. ### How do you migrate to new on-call software? Inventory services and owners, audit which alerts need a human, define coverage and escalation, build schedules with overrides and holidays, attach runbooks and dashboards, verify delivery channels, test failure paths, pilot with one service group, and plan cutover and rollback so only one system is authoritative at a time. ### What can on-call software not fix? Alerts that need no immediate action, services without a current owner, rotations too small to allow recovery, unprepared backups, responders without authority to mitigate, and stale runbooks. Good on-call software makes these problems visible, and the engineering organization still has to act on them. --- # A comprehensive guide to incident postmortems URL: https://rootly.com/incident-postmortems Pillar: Incident Postmortems A good postmortem turns one incident into better systems, clearer ownership, and fewer repeat failures by helping the team understand what really happened. **An incident postmortem is a structured review conducted after an incident to understand what happened, why it happened, and how similar issues can be prevented in the future.** If you’re operating a system with real users, incidents are inevitable. A failed deployment, a missed alert, or an unexpected third-party outage can happen at any time. What separates high-performing teams is not avoiding incidents altogether, but responding effectively, learning from every failure, and continuously improving. That is where postmortems become essential. More than a meeting or a document, they provide a repeatable process for analyzing incidents, uncovering contributing factors, and turning lessons into measurable improvements. [Reliable on-call software](https://rootly.com/on-call-software) supports this workflow by improving alert routing, escalation, incident coordination, and timeline capture, giving teams the accurate data and shared context they need to conduct meaningful reviews. When practiced consistently, postmortems strengthen both technical operations and organizational culture. They align engineering, product, support, and leadership around a shared understanding of each incident while promoting accountability, transparency, and continuous learning. Whether you're creating your first postmortem process or refining an existing one, every incident becomes an opportunity to build more reliable systems and more resilient teams. ## Key Takeaways - Postmortems are for learning, not proving who was at fault. - The strongest reviews reconstruct what happened before deciding why. - A lesson only improves reliability when it becomes owned, tracked work. - Consistent templates make honest reviews easier to run and easier to reuse. ## What Is an Incident Postmortem? ![Illustration for “What Is an Incident Postmortem?”](https://rootly.com/assets/incident-postmortems-beginners-guide-to-incident-postmortems-what-is-an-.avif) A **postmortem** (sometimes called an incident review or retrospective) is a formal review that takes place after an incident has occurred. The **process** involves gathering the team to discuss the incident in a blameless, structured format. The **document** serves as a record of the timeline, root cause, impact, resolution steps, and follow-up actions. Postmortems are not about assigning blame. They exist to uncover system weaknesses, process gaps, and organizational blind spots so that similar issues can be prevented or better mitigated next time. ## Benefits of Running Postmortems Whether you’re running a small startup or a global infrastructure team, postmortems offer immense value: ### Internal Benefits - Foster team trust through transparency and psychological safety - Drive faster incident resolution through knowledge sharing - Create consistent processes for learning and accountability - Prevent recurrence by surfacing root causes, not symptoms ### External Benefits - Improve transparency with stakeholders through structured reporting - Build user trust by demonstrating a culture of continuous improvement - Show compliance with SLAs, SLOs, and industry standards Postmortems are not just operational tools—they are cultural anchors for engineering resilience. ## Why Run a Postmortem? Your infrastructure isn't just made of servers and code—it’s a living, breathing system that includes people, vendors, software, users, and the most unpredictable element of all: time. That complexity makes failure inevitable. But if treated correctly, failure becomes a teacher, not just a setback. Systems that learn from disruption are considered _antifragile_—they grow stronger in the face of uncertainty, disorder, and error. Postmortems are your pathway to antifragility. Every incident reveals something new. Often, what emerges is a hidden interdependency that wasn't visible until stress exposed it. For example: Imagine a system where: - Component A connects to both B and C - There’s no apparent connection between B and C - Each process from A opens simultaneous connections to B and C ![Illustration for “Why Run a Postmortem?”](https://rootly.com/assets/incident-postmortems-beginners-guide-to-incident-postmortems-why-run-a-p.avif) Now suppose B slows down. A queues more processes, each opening connections to C. Eventually, C gets overwhelmed—even though it was B that faltered. You've now uncovered a hidden link between B and C that didn't exist in your mental model before. ![Illustration for “Why Run a Postmortem?”](https://rootly.com/assets/incident-postmortems-beginners-guide-to-incident-postmortems-why-run-a-p-2.avif) **That’s the power of a postmortem.** It gives you the opportunity to update your understanding of the system and document not only how to prevent the incident, but how to respond faster and more effectively if it happens again. A good postmortem isn’t just a record—it’s a mechanism for _evolving your model of reality_. ## An effective incident postmortem plan A strong postmortem process doesn’t happen by accident. It’s designed, documented, and continuously refined. The most effective postmortem plans are clear enough to be followed under pressure and flexible enough to adapt to different types of incidents. Here’s what to include in your postmortem plan: ### 1. Define When a Postmortem Is Required Establish clear criteria for when a postmortem must be triggered—based on [severity level](https://rootly.com/incident-response/support-levels), SLA breach, user impact, or operational disruption. This avoids ambiguity and ensures consistent follow-through. ### 2. Assign Ownership Early Designate a postmortem facilitator—typically someone from the **incident response team**, such as the incident commander, SRE, or engineering lead—responsible for guiding the process. Make it clear who documents the event, collects inputs, and drives follow-up. ### 3. Use a Standard Template Standardize your postmortem structure with a shared template. This keeps documentation consistent across teams and ensures important elements like timeline, impact, and action items are never missed. ### 4. Schedule the Review Promptly Time matters. Add postmortem reviews to the calendar within 24 to 48 hours after incident resolution. Choose a time when all key stakeholders can attend and the event is still fresh in memory. ### 5. Encourage Blameless Participation Reinforce a no-blame culture throughout the process. Frame questions around system behavior and process design—not individual decisions. Create psychological safety so contributors feel comfortable sharing openly. ### 6. Focus on Actionable Outcomes Every postmortem should result in a small set of clear, prioritized action items. Assign ownership, set deadlines, and follow up regularly. Avoid vague conclusions like “be more careful” or “monitor more closely.” ### 7. Review and Iterate Regularly Treat your postmortem process like any other part of your system. Hold periodic retrospectives on how well it’s working. Are documents being written? Are follow-ups happening? Is the process helping teams learn and improve? ## When Should You Run a Postmortem? Not every incident warrants a full postmortem, but many do. A good rule of thumb: ### Run a postmortem when an incident: - Breaches SLAs/SLOs (e.g., uptime, latency, availability) - Impacts a significant number of users or key stakeholders - Requires cross-functional coordination or emergency escalation - Could recur if underlying causes aren’t addressed ### Optional but useful postmortems include: - Near-misses where luck prevented major damage - Recurring minor incidents that point to systemic issues - Major planned interventions (e.g., infrastructure overhauls) **Best practice:** Run the postmortem within 48 hours of incident resolution. The closer to the event, the fresher the memory and the more accurate the timeline. ## Proactive vs. Reactive Postmortems Most teams think of postmortems as reactive: something you do after a failure. But proactive postmortems are just as valuable. ### Reactive Postmortem - Triggered by an actual incident - Focuses on recovery, root cause, and future prevention ### Proactive Postmortem - Triggered by a near-miss, emerging trend, or recurring issue - Helps surface systemic vulnerabilities before they escalate Running both types gives your team a more complete picture of risk, helping you stay ahead of future problems. ## Do’s and Don’ts of Running Postmortems ### Do: - Foster a blameless environment: Focus on the process, not the person - Involve the right stakeholders: Engineers, ops, and business teams - Use data and logs to reconstruct the timeline objectively - Assign clear, actionable takeaways with owners and due dates ### Don’t: - Point fingers or assign blame - Skip the write-up—verbal discussions alone don’t scale - Delay the postmortem until memory fades - Overcomplicate: Keep the format consistent and lightweight ### Examples: - _Poor behavior:_ "Alice deployed broken code." - _Ideal behavior:_ "A lack of automated tests allowed faulty code to be deployed without detection." ## Key Components of a Postmortem Document A well-structured postmortem should include: 1. **Summary**: What happened in 1-2 sentences 2. **Timeline**: Chronological account with timestamps 3. **Impact**: Who/what was affected and how badly 4. **Root Cause Analysis**: Technical and process-level causes 5. **Resolution Steps**: How the issue was identified and fixed 6. **Action Items**: Preventative tasks with clear ownership 7. [**Metrics**](https://rootly.com/incident-response/metrics): MTTR, duration, severity level, incident cost (if applicable) Optional additions: - Screenshots or logs - Charts or metrics snapshots - Links to Slack threads, Jira tickets, or [runbooks](http://runbooks) ## What to Do With the Postmortem Document Creating the document is only half the job. To make it impactful: - **Store in a central, searchable location** (e.g., Confluence, Notion, Rootly) - **Tag with relevant metadata** (incident type, affected system, date, severity) - **Circulate internally to stakeholders** (product, support, leadership) - **Review in monthly ops or engineering review meetings** **External sharing:** Only share externally if it’s properly anonymized, reviewed by comms/legal, and framed constructively. Transparency builds trust but must be handled with care. ## Tools, Templates, and Automation Speed and consistency matter. Use: - **Incident postmortem templates** to standardize structure - **Incident management tools** (e.g., Rootly, PagerDuty) - **Automated timeline builders** to pull Slack, logs, and alerts - **Checklists** for facilitators and participants - **Recurring calendar reminders** to review past postmortems monthly Automation ensures nothing gets missed when adrenaline is high. ## Turning Incidents Into Insight Postmortems aren’t about fault. They’re about building better systems and stronger teams. By leaning into incident reviews with a blameless, consistent process, you create a culture of curiosity, learning, and resilience. Every incident is an opportunity to improve. Don’t let that opportunity go to waste. Start small, stay consistent, and let your postmortem process evolve with your team. ## Frequently asked questions ### What is an incident postmortem? A structured review held after an incident to work out what happened, why it happened, and how to prevent it or handle it better next time. It covers the review meeting and the written record: timeline, root cause, impact, resolution steps and follow-up actions. Many teams call it an incident review or a retrospective. ### When should you run a postmortem? Run one when an incident breaches an SLA or SLO, affects a significant number of users or key stakeholders, needs cross-functional coordination or emergency escalation, or could recur if its underlying causes stay in place. Near-misses and recurring minor incidents are worth reviewing too. ### How soon after an incident should the postmortem happen? Within 24 to 48 hours of resolution, while memories are fresh and the timeline is still easy to reconstruct. ### What should a postmortem document include? A one- or two-sentence summary, a timestamped timeline, the impact, root cause analysis covering technical and process causes, the resolution steps, action items with clear owners, and metrics such as MTTR, duration, severity and incident cost where it applies. ### What makes a postmortem blameless? It examines system behavior and process design instead of individual decisions. Instead of 'Alice deployed broken code', a blameless review writes 'A lack of automated tests allowed faulty code to be deployed without detection.' ### What is the difference between a reactive and a proactive postmortem? A reactive postmortem follows an actual incident and focuses on recovery, root cause and prevention. A proactive postmortem is triggered by a near-miss, an emerging trend or a recurring issue, and looks for weaknesses before they cause an incident. --- # How to build an incident response communication plan URL: https://rootly.com/incident-communications Pillar: Incident Communications A good communication plan keeps responders aligned and gives customers, leaders, and partners useful updates without distracting the people working on the fix. After years of training and over a decade of investments, you’re aboard Apollo 13, on your way to the moon. The whole world is watching the mission closely. From the launch and as you leave Earth’s orbit, it’s all excitement and constant monitoring. Two days into the journey, you hear unexpected noises. Your dashboards are showing concerning signs. Your life, and everyone else’s aboard, is in danger. **“Houston, we’ve had a problem,”** you tell mission control, maintaining your cool even though your chances of survival are slim. It’s been over 50 years, but the safe return of Apollo 13’s crew to Earth remains one of the most emblematic incidents where communication played a crucial role. The astronauts’ exhaustive communications with HQ, along with the coordination of dozens of engineers back on Earth, made it possible to put together a survival plan in the most extreme conditions. Apollo 13 had over fifteen communication protocols with specific channels, dedicated teams, and precise terminology. The accidental oxygen tank explosion aboard was not a foreseeable incident, but having a communication plan for dealing with the unexpected was crucial to make failure not an option. Having an incident response communication plan is not a nice-to-have feature. Effective communication can significantly mitigate the impact of an incident. Good communication can smooth out your customers' experience, soothe concerned shareholders, and protect your brand reputation. ## Key takeaways - Decide who communicates, who approves messages, and which audiences need updates before an incident begins. - Let severity and customer impact determine the channels, detail, and cadence of communication. - Tell people what is known, what the team is doing, and when the next update will arrive. - Templates can save time, but clear ownership and a reliable source of truth are what preserve trust. ## What is an incident communication plan and why every company needs one No service or team is perfect, which means you’ll inevitably run into incidents. Undoubtedly, your response team will be hard at work to resolve any incident as quickly as possible. However, the perception that impacted users, shareholders, and other teammates have of the incident is just as important as what is actually happening. How you handle an incident can strengthen your relationship with customers and partners and protect your brand reputation. But you can’t improvise communications while managing expectations during and after an incident—you’ll need to have an effective Incident Communication Plan ready. An [Incident Communication Plan](https://rootly.com/incident-communications) is a framework that outlines how and when an organization communicates with different audiences during and after an incident. An Incident Communication Plan may include: - Definitions for [incident severity levels](https://rootly.com/incident-response/support-levels) and their implications on communications. - A breakdown of the audiences (customers, partners, investors, etc.) that need to be kept in the loop during and after an incident. - Descriptions of [incident response roles and responsibilities](https://rootly.com/blog/how-to-build-an-effective-incident-response-team-step-by-step-guide) for writing and approving messages or addressing impacted accounts. - A list of communication channels and guidelines on how and when to use each. However, an Incident Communication Plan is not a list of canned messages or generic copy meant to be sent to customers. In fact, I recommend the SRE teams I coach to avoid those practices. Every incident is different, and your communication should be carefully crafted for each case. ## How should teams communicate during a major outage? Acknowledge within minutes, then update on a fixed cadence even when nothing has changed. Give each audience its own message: technical detail for responders in the incident channel, impact and timeline for leadership and customer-facing teams, and plain status for customers on the status page. A communications lead owns all of it, so the engineers working the fix aren't interrupted. Rootly runs all three streams from the incident in Slack or Microsoft Teams. The [full playbook for major-outage communication](https://rootly.com/incident-response/communication#how-should-teams-communicate-during-a-major-outage) covers the audience table and the stakeholder cadence in detail. In Rootly, all three streams run from the incident in Slack or Microsoft Teams: the status page is updated from the incident channel, reminders flag overdue updates, and Rootly AI drafts each update at the right level of detail for its audience. ## How to build your incident response communication plan ### Identify key stakeholders An effective Incident Response Communication Plan ensures everyone who needs to know about an incident does so. Thus, you need to identify who those people are and when and how to contact them during an incident. You may also outline the type of details that are relevant to each stakeholder. Examples of key stakeholders may include: - Customers - External partners - Executives - Internal teams - Media partners - Regulatory bodies ### Define incident severity levels and act accordingly Depending on the incident’s scope and impact, you will have different communication needs. For example, minor incidents may not need to be communicated to unaffected customers, while for security incidents, you may need to coordinate with law enforcement agencies. While some communication elements may seem “obvious” as you read this, things can get quite confusing when you’re dealing with an active incident. That’s why outlining severity levels and their potential communication implications is a key part of your Incident Response Communication Plan. ### Establish internal communication protocols You’ve established who needs to be contacted and under which circumstances by defining key stakeholders and incident severity tiers. Now, you need to pin down how you’ll get in contact with each of them and set expectations for communication cadence. For example, for internal communication, I recommend you keep incident collaboration within your usual platforms, such as Slack or Microsoft Teams. That may mean having a dedicated incident channel where responders actively collaborate, while higher-level updates can be shared by a [Slack bot like Rootly’s](https://rootly.com/incident-response) in a different channel for org-wide visibility. Establish guidelines on what information should remain confidential and what can be shared externally, as well as how to manage those communications. ### Develop external communication strategies While your response team needs to remain nimble and not overly bound by protocol to collaborate and mitigate an incident, communication with third parties should be very intentional. You want to avoid triggering unnecessary panic from your customers and ensure there are no misunderstandings from your partners or investors. External communications are best handled by a teammate with a strong grasp of technology, a deep understanding of your business, and, ideally, media training. It is common to have individuals responsible for each external stakeholder, such as Investor Relations, Customer Relations, or Press Relations. Collaborate with each of them to develop an external communications strategy that the response team can rely on during an incident. This ensures they will feel comfortable reaching out to the right person at the right time. ## Incident communication best practices ### Prioritize clarity and transparency Transparency does not necessarily mean sharing specific technical details of an incident. While technical details may clarify the situation for a few people, it’s unlikely to help others—even those within your organization—understand what to expect. Instead, I recommend writing messages that make sense to your audience. For example, when writing a status page update, provide details on what users can expect from the degraded services. When explaining the situation to executives, focus on explaining the impacted accounts. Keep in mind that not all your audiences are technical but still require updates that help them understand what’s going on and how you’re mitigating the situation. Try to translate technical issues into simplified terminology that your audience can grasp intuitively. ### Ensure timeliness in communication Too frequent communications can become noise and take away too much of your time. However, not enough communication can be misunderstood by stakeholders as a lack of progress in mitigating the incident. Your Incident Response Communication Plan can include details on how frequently each audience and channel should be updated. This can help alleviate tensions with stakeholders by setting clear expectations. Tools like [Rootly AI](https://rootly.com/ai-sre) can also help you shorten the time needed to write updates for each audience, as they can generate summaries for specific stakeholders based on the incident’s context. Use those summaries as a starting point to [save up to 51% of the time writing them](https://rootly.com/blog/how-meta-and-google-use-ai-to-improve-incident-response), as Google has done. ### Sound like a human You can skip the “Dearest customer, we sincerely regret any inconvenience caused” in most cases. Nobody appreciates corporate jargon or generic phrases being thrown at them while they’re in distress. Include a section on style and tone in your Incident Response Communication Plan so everyone is aligned on how to provide a cohesive voice for your company. Emphasize making your messaging sound empathetic, showing you’re human and care about those affected. ### Own it You’ve seen the typical “an error was made” in incident communication messages. For your audience, this language is unhelpful and can come across as if you're deflecting responsibility for what is happening. Acknowledge the issue directly and provide details that reassure your audience you’re doing everything possible to resolve the incident. Be specific if you need action from your reader—for example, if you need your customers to reconnect their authentication service. ## How Rootly can help you manage incident communication Rootly is an [on-call and incident management solution](https://rootly.com/) that helps you orchestrate communication during and after an incident. You can set up [automated workflows](https://rootly.com/incident-response) to notify relevant stakeholders when certain conditions are met, or generate AI-powered summaries based on the incident context. ## Frequently asked questions ### What is an incident communication plan? A predefined plan that sets communication roles, audiences, channels, message guidelines, and update cadence so teams can share accurate information under pressure. ### Who owns communication during an incident? Usually a dedicated communications lead who turns technical status into useful updates for responders, leadership, customers, and other stakeholders while the technical team focuses on recovery. ### How often should incident updates be sent? Cadence should increase with severity and customer impact. Every update should say what is known, what the team is doing, and when readers can expect the next update. ### How should teams communicate during a major outage? Acknowledge within minutes, update on a fixed cadence, and give each audience its own message: technical detail for responders, impact and timeline for leadership, and plain status for customers on the status page. Rootly runs all three from the incident in Slack or Microsoft Teams, with Rootly AI drafting each update. ### How can we keep customers informed without distracting engineers during an outage? Give status page and stakeholder updates to a communications lead and draft them from the incident timeline. Rootly AI drafts customer-facing updates from the timeline, so the communications lead edits a draft instead of interrupting responders. --- # Deeper articles — summaries and where to fetch them ## AI SRE — 11 articles Full text of every article below: https://rootly.com/llms/ai-sre.txt - **What AI SRE observes, infers, and is allowed to do** — When an incident gets messy, AI SRE has to do more than sound convincing. I examine the principles that keep its evidence inspectable, its hypotheses testable, and its production actions constrained. https://rootly.com/ai-sre-guide/concepts · https://rootly.com/ai-sre-guide/concepts.md - **AI SRE architecture: how to build for incomplete context** — An AI agent can have the right code and the wrong context. See how AI SRE architecture connects evidence, policy, execution, verification, and rollback. https://rootly.com/ai-sre-guide/architecture · https://rootly.com/ai-sre-guide/architecture.md - **Where AI helps (and fails) across the incident lifecycle** — AI can support every stage of an incident by carrying context forward, while responders retain authority over decisions and production changes. https://rootly.com/ai-sre-guide/lifecycle · https://rootly.com/ai-sre-guide/lifecycle.md - **A practical AI SRE maturity model: from investigation to guarded automation** — Teams earn greater autonomy gradually, by proving that their evidence, controls, and operational data can support it safely. https://rootly.com/ai-sre-guide/maturity-model · https://rootly.com/ai-sre-guide/maturity-model.md - **AI SRE metrics and ROI: how to measure impact beyond MTTR** — The value of AI SRE appears first in faster context, safer actions, and less on-call toil, not necessarily in a dramatic change to MTTR. https://rootly.com/ai-sre-guide/metrics-and-roi · https://rootly.com/ai-sre-guide/metrics-and-roi.md - **AI SRE implementation: how to run a safe, staged pilot** — A successful AI SRE rollout builds trust in stages, beginning with better incident context and introducing controlled action only when the workflow is ready. https://rootly.com/ai-sre-guide/implementation-guide · https://rootly.com/ai-sre-guide/implementation-guide.md - **AI SRE integrations: the data an incident investigation actually needs** — AI SRE becomes useful when it can connect an alert to the change, service owner, runbook, and incident history behind it. https://rootly.com/ai-sre-guide/integrations · https://rootly.com/ai-sre-guide/integrations.md - **AI SRE vs AIOps: what each does and when you need both** — AIOps works on the event stream: noise reduction, correlation and, in some platforms, root-cause analysis. AI SRE works the incident across every tool. How the two differ, where they overlap, and whether you need both. https://rootly.com/ai-sre-guide/ai-sre-vs-aiops · https://rootly.com/ai-sre-guide/ai-sre-vs-aiops.md - **AI SRE use cases by industry: Where AI changes reliability most** — The mechanics of AI SRE stay consistent across industries, but the acceptable risk and cost of a wrong decision change considerably. https://rootly.com/ai-sre-guide/use-cases-by-industry · https://rootly.com/ai-sre-guide/use-cases-by-industry.md - **AI SRE FAQ: safety, security, and adoption questions answered** — The practical questions around AI SRE are less about what a model can generate and more about data access, oversight, security, and trust. https://rootly.com/ai-sre-guide/frequently-asked-questions · https://rootly.com/ai-sre-guide/frequently-asked-questions.md - **What is AI incident management and how does it work?** — AI incident management applies AI across the incident lifecycle: routing alerts, investigating the cause, coordinating responders and writing the record. How each stage works, how it stays safe in production, and what to check before trusting it. https://rootly.com/ai-sre-guide/ai-incident-management · https://rootly.com/ai-sre-guide/ai-incident-management.md ## Alert Management — 12 articles Full text of every article below: https://rootly.com/llms/alert-management.txt - **Alert fatigue: causes, risks, and how to reduce notification overload** — Discover what causes alert fatigue, its impact on incident response, and how smarter alert management reduces noise and improves reliability at scale. https://rootly.com/alert-management/alert-fatigue · https://rootly.com/alert-management/alert-fatigue.md - **Alert deduplication and correlation: how modern teams reduce duplicate alerts** — Alert deduplication and correlation group related signals while preserving the evidence responders need to assess impact and diagnose incidents https://rootly.com/alert-management/alert-deduplication-and-correlation · https://rootly.com/alert-management/alert-deduplication-and-correlation.md - **Alert routing: how to send alerts to the right responders** — Effective alert routing uses service ownership, schedules, and fallbacks to get urgent signals to the right responder. https://rootly.com/alert-management/alert-routing · https://rootly.com/alert-management/alert-routing.md - **Alert escalation policies: how automated escalation improves incident response** — Learn how automated alert escalation policies reduce missed alerts, improve response times, and ensure critical incidents reach the right responders. https://rootly.com/alert-management/alert-escalation-policies · https://rootly.com/alert-management/alert-escalation-policies.md - **Alert noise reduction: how to eliminate alert noise without missing critical incidents** — Learn how to reduce alert noise using filtering, suppression, correlation, and smarter thresholds without missing critical incidents or service risks. https://rootly.com/alert-management/alert-noise-reduction · https://rootly.com/alert-management/alert-noise-reduction.md - **Alert management integrations: connecting monitoring, collaboration, and incident response tools** — A reliable alert workflow depends on integrations across monitoring, on-call, collaboration, and incident response, with clear sources of truth. https://rootly.com/alert-management/alert-management-integrations · https://rootly.com/alert-management/alert-management-integrations.md - **Alert management automation: automating routing, escalation, and incident response** — Learn how alert management automation improves routing, escalation, enrichment, runbooks, and incident response to reduce MTTR and alert noise faster. https://rootly.com/alert-management/alert-management-automation · https://rootly.com/alert-management/alert-management-automation.md - **AI-powered alert management: how AI improves alert triage and response** — AI can strengthen alert triage and response when it has the operational context, evidence, and controls required to earn engineers’ trust. https://rootly.com/alert-management/ai-powered-alert-management · https://rootly.com/alert-management/ai-powered-alert-management.md - **Alert management best practices for reliable incident response** — Learn alert management best practices for improving alert quality, reducing noise, setting thresholds, assigning ownership, and strengthening incident response. https://rootly.com/alert-management/alert-management-best-practices · https://rootly.com/alert-management/alert-management-best-practices.md - **Alert management metrics: 18 KPIs every engineering team should track** — Track the 18 essential alert management metrics engineering teams need to reduce noise, improve response times, strengthen routing, and support healthier on-call operations. https://rootly.com/alert-management/alert-management-metrics · https://rootly.com/alert-management/alert-management-metrics.md - **How to choose alert management software: a buyer’s guide** — What matters when choosing alert management software: routing, integrations, automation, reliability, security, and total cost. https://rootly.com/alert-management/how-to-choose-alert-management-software · https://rootly.com/alert-management/how-to-choose-alert-management-software.md - **Best 9 alert management software for engineering teams in 2026** — Compare the best alert management software for engineering teams in 2026, including features, use cases, pricing factors, and platform selection tips. https://rootly.com/alert-management/best-9-alert-management-software-for-engineering-teams-in-2026 · https://rootly.com/alert-management/best-9-alert-management-software-for-engineering-teams-in-2026.md ## Incident Response — 12 articles Full text of every article below: https://rootly.com/llms/incident-response.txt - **Incident response process: A step-by-step guide for SRE teams** — A dependable response process gives SRE teams a shared path from the first alert through recovery and review, even when the incident itself is unfamiliar. https://rootly.com/incident-response/lifecycle-process · https://rootly.com/incident-response/lifecycle-process.md - **Incident response best practices for modern teams** — Good incident response is built before anything breaks: prepare the team, make decisions visible, practice the hard moments, and learn from every real one. https://rootly.com/incident-response/best-practices · https://rootly.com/incident-response/best-practices.md - **Incident commander: role and responsibilities** — The incident commander creates order without becoming the bottleneck, setting priorities, assigning ownership, and keeping the response moving toward recovery. https://rootly.com/incident-response/incident-commander · https://rootly.com/incident-response/incident-commander.md - **How to structure an incident response team: Roles, responsibilities, and workflows** — Clear roles stop capable responders from colliding under pressure and give technical work, communication, documentation, and leadership each a responsible owner. https://rootly.com/incident-response/team-roles · https://rootly.com/incident-response/team-roles.md - **Incident Response Playbooks: How to Build and Use Them** — A useful playbook captures the decisions a scenario needs, so responders coordinate instead of improvising in the middle of an incident. https://rootly.com/incident-response/playbooks · https://rootly.com/incident-response/playbooks.md - **Incident response runbooks: templates and examples** — Runbooks turn known failure modes into executable steps, but they only earn trust when they are easy to find, safe to follow, and kept current. https://rootly.com/incident-response/runbooks · https://rootly.com/incident-response/runbooks.md - **Incident Priority vs Severity: P1, P2, P3 and SEV Levels** — Severity (SEV0 to SEV3) measures an incident's impact. Priority (P0 to P3) sets how fast you act. The difference, with a severity and priority matrix. https://rootly.com/incident-response/support-levels · https://rootly.com/incident-response/support-levels.md - **Incident communication plans: internal and external** — During an incident, clear communication reduces uncertainty for responders, leaders, and customers while the technical team works toward recovery. https://rootly.com/incident-response/communication · https://rootly.com/incident-response/communication.md - **Incident Response Metrics: MTTD, MTTR, MTTA and More** — Incident metrics are most useful when they expose where response slows down and help teams improve the system, not grade individual responders. https://rootly.com/incident-response/metrics · https://rootly.com/incident-response/metrics.md - **Incident Response Tools: How to Choose the Right One** — The right incident response platform connects alerts, people, context, and follow-up work so coordination does not become another failure mode. https://rootly.com/incident-response/tools · https://rootly.com/incident-response/tools.md - **Incident response in aviation, finance, and healthcare: Lessons for every team** — Aviation, finance, and healthcare show what disciplined response looks like when preparation, communication, and learning are inseparable from safety and trust. https://rootly.com/incident-response/industry-examples · https://rootly.com/incident-response/industry-examples.md - **The role of automation in incident response** — Automation runs the incident response steps a team can define in advance, so responders spend the incident on diagnosis and decisions. What to automate at each stage, what to keep human, and where to start. https://rootly.com/incident-response/automation · https://rootly.com/incident-response/automation.md ## On-Call Operations — 10 articles Full text of every article below: https://rootly.com/llms/on-call-software.txt - **On-call rotation design: schedules that avoid burnout** — A well-designed rotation makes coverage predictable and recovery possible; a poor one turns reliability into an uneven tax on the same few engineers. https://rootly.com/on-call-software/schedules-and-rotations · https://rootly.com/on-call-software/schedules-and-rotations.md - **On-Call Pay: Compensation Models and Benchmarks** — On-call compensation works when it reflects the real burden of readiness and gives teams a transparent way to account for time, disruption, and workload. https://rootly.com/on-call-software/pay · https://rootly.com/on-call-software/pay.md - **Alert fatigue: how to reduce noise and protect on-call engineers** — Alert fatigue begins when every signal demands attention, leaving responders slower to recognize the few notifications that truly require action. https://rootly.com/on-call-software/alert-fatigue · https://rootly.com/on-call-software/alert-fatigue.md - **A manager’s guide to improving on-call: Leadership, fairness, and team wellbeing** — A healthier on-call program starts with leadership choices about fairness, ownership, recovery time, and how the team learns when something goes wrong. https://rootly.com/on-call-software/improving-on-call · https://rootly.com/on-call-software/improving-on-call.md - **Escalation and coverage in on-call for modern engineering** — Escalation should remove uncertainty during an incident by making it clear who responds first, when backup joins, and how coverage is maintained. https://rootly.com/on-call-software/escalation-policies · https://rootly.com/on-call-software/escalation-policies.md - **On-call tools: Choosing the right platform for your team** — The right on-call platform reaches the correct responder, carries enough context to act, and fits the incident workflow the team already uses. https://rootly.com/on-call-software/tools · https://rootly.com/on-call-software/tools.md - **On-call metrics and analytics: Track what matters** — On-call metrics are most useful when they reveal both response performance and the human cost of keeping services covered. https://rootly.com/on-call-software/metrics · https://rootly.com/on-call-software/metrics.md - **Building a sustainable on-call culture: Training, debriefs, and team wellbeing** — A sustainable on-call culture prepares people before incidents, supports them during response, and turns difficult shifts into shared learning. https://rootly.com/on-call-software/culture-training · https://rootly.com/on-call-software/culture-training.md - **Automating on-call: How modern teams cut costs, downtime, and burnout** — On-call automation is valuable when it removes repetitive work and noisy interruptions without hiding consequential decisions from responders. https://rootly.com/on-call-software/automation · https://rootly.com/on-call-software/automation.md - **On-call management strategies for fintech, healthcare, and more** — The foundations of on-call stay consistent across industries, but the consequences, compliance needs, and escalation paths vary with the systems at risk. https://rootly.com/on-call-software/industry-examples · https://rootly.com/on-call-software/industry-examples.md ## Incident Postmortems — 7 articles Full text of every article below: https://rootly.com/llms/incident-postmortems.txt - **How to run a postmortem meeting: agenda and template** — A useful postmortem meeting turns scattered evidence into a shared timeline, clear lessons, and follow-up work people actually own. https://rootly.com/incident-postmortems/meeting-guide · https://rootly.com/incident-postmortems/meeting-guide.md - **How to run effective blameless postmortems** — Blameless postmortems replace fault-finding with honest systems thinking, so people can share what they saw and teams can fix what made the failure possible. https://rootly.com/incident-postmortems/blameless · https://rootly.com/incident-postmortems/blameless.md - **Incident postmortems: turning failures into actionable insights** — A practical postmortem template gives every review enough structure to capture what happened, what mattered, and what the team will change next. https://rootly.com/incident-postmortems/template · https://rootly.com/incident-postmortems/template.md - **Root cause analysis for software incidents: from cause to verified fix** — How engineering teams run root cause analysis on production incidents: the methods that work, how to turn contributing factors into owned fixes, and how to stop the same incident happening again. https://rootly.com/incident-postmortems/root-cause-analysis · https://rootly.com/incident-postmortems/root-cause-analysis.md - **Why timeline construction matters in retrospectives and incident postmortems** — A reliable timeline replaces conflicting memories with a shared sequence of events, making delays, decisions, and missed signals easier to understand. https://rootly.com/incident-postmortems/timeline · https://rootly.com/incident-postmortems/timeline.md - **Public vs. private incident postmortems: How to choose, communicate, and build trust** — The right level of transparency depends on who needs the truth, which details are safe to share, and how clearly the team can explain what comes next. https://rootly.com/incident-postmortems/public-vs-private · https://rootly.com/incident-postmortems/public-vs-private.md - **Incident report template: what a good software incident report includes** — A copyable incident report template for software teams, what every field is for, and how to keep incident reports consistent across teams by capturing them while the incident happens. https://rootly.com/incident-postmortems/incident-report-template · https://rootly.com/incident-postmortems/incident-report-template.md --- # Topic bundles - AI SRE: https://rootly.com/llms/ai-sre.txt - Alert Management: https://rootly.com/llms/alert-management.txt - Incident Response: https://rootly.com/llms/incident-response.txt - On-Call Operations: https://rootly.com/llms/on-call-software.txt - Incident Postmortems: https://rootly.com/llms/incident-postmortems.txt - Incident Communications: https://rootly.com/llms/incident-communications.txt - Competitor comparisons: https://rootly.com/llms/comparisons.txt --- # FAQ ### Platform What does Rootly include? One platform covering the full incident lifecycle: on-call/paging, incident response in Slack or Microsoft Teams, AI SRE investigation, retrospectives, and status pages - with a service catalog, workflows, and automations. AI agents and status pages come with Incident Response, with no per-feature add-ons; AI SRE is an additional product that requires Incident Response. Is Rootly just for Slack? No. The full incident lifecycle runs natively in both Slack and Microsoft Teams - declaration, coordination, comms, AI assistance, and retrospectives - without switching to a separate interface. Is Rootly a monitoring tool? No. Rootly integrates with monitoring tools (Datadog, Sentry, Grafana, Prometheus, Honeycomb, and others) and focuses on incident management: on-call, response, learning, and status pages. ### On-Call How does Rootly decide who gets paged? Rootly reads the alert's routing target - service, team, or escalation policy - then uses live schedules and escalation rules to select the responder, escalating to the next level until someone acknowledges. It can page teams, users, schedules, and Slack channels directly, with no workaround "services" required. What channels can Rootly page through? Voice calls, SMS, push notifications, Slack, and email - chosen by urgency and user preference, with automatic retries, fallbacks, and Do Not Disturb override for critical alerts. Can Rootly handle complex schedules? Yes - multi-person rotations, layered primary/secondary coverage, business-hours-only and time-zone-aware schedules, overrides, shadow rotations, and one-click coverage requests. Slack user groups update automatically. What is live call routing, and what are heartbeats? Live call routing turns a phone call into a page (live connection, voicemail-to-page, IVR trees, failover). Heartbeats detect silent failures: if a system stops checking in within its expected interval, Rootly alerts the right team and auto-resolves on a recovery ping. How reliable is Rootly's paging? Rootly runs on a multi-region redundant architecture with a 99.99% uptime SLA and multi-channel fallbacks, so a critical alert lands even if one channel or provider is degraded. ### Incident Response What is Rootly Incident Response? Teams run the entire incident inside Slack or Microsoft Teams - declaring, assigning roles, coordinating the fix, updating stakeholders, and capturing the timeline. Each incident inherits context from on-call, the service catalog, and past incidents. What can Rootly's in-channel AI do? Mention @Rootly to ask questions in plain language and take action as your user, within your permissions - paging, changing severity/status, managing action items, and drafting comms. It also provides catch-up summaries, incident summaries, and a meeting scribe for bridge calls, and confirms destructive actions before running them. How does Rootly keep stakeholders updated? Rootly automates stakeholder, leadership, and customer communication and keeps the status page current as the incident changes state, so responders stay focused on the fix. Can I manage incident response programmatically? Yes. Rootly offers a full REST API, official SDKs (Python, Go, Ruby, JavaScript), a Terraform provider for config-as-code, and an MCP server for IDE agents like Cursor, Windsurf, and Claude. ### AI SRE What is an AI SRE? An AI system that performs the investigation, triage, and coordination tasks traditionally handled by a site reliability engineer during an incident - monitoring alerts, running root cause analysis, and assisting resolution alongside human engineers, not replacing them. What is Rootly AI SRE and how does its root cause analysis work? Rootly AI SRE activates the moment an alert fires and runs parallel hypothesis checks across alerts, telemetry, recent deployments, and past incidents, producing a ranked, evidence-backed theory of what went wrong. Each finding includes a confidence score and a visible reasoning chain. What does "human in the loop" mean in practice? Rootly AI investigates, surfaces findings, suggests fixes, and drafts updates - but every change requires explicit human sign-off before execution. It never auto-remediates on its own. How quickly does Rootly AI SRE deliver value? Teams typically see it surface its first root cause finding within the first incident after setup, with no separate onboarding because it's built into the existing platform. ### Retrospectives What's the difference between a postmortem and a retrospective? They're often used interchangeably, but "postmortem" emphasizes documenting what happened, while "retrospective" emphasizes learning and improving the system and process through tracked follow-up actions. Rootly uses blameless retrospectives. What parts of a retro can AI safely automate? Assembling the timeline (from alerts, chat, commits/deploys, and status updates), summarizing long threads, drafting sections, and generating initial action items - all editable so humans validate the facts and final commitments. How do you run blameless retros without losing accountability? Focus on what in the system made the outcome likely - gaps in alerts, runbooks, ownership, or change practices - then assign clear owners and due dates. Accountability is about follow-through, not blame. Do Rootly retrospectives work with our existing tools? Yes. Action items push to Jira or Linear as synced subtasks, and retro docs export to Confluence, Notion, Google Docs, and SharePoint, so the retro lives where your org documents knowledge. ### Status Pages Why use a status page, and how does Rootly's reduce support tickets? A status page is a single source of truth during downtime - what's impacted, what you're doing, and when the next update lands. When customers can self-serve accurate updates, they stop opening duplicate "is it down?" tickets, and Rootly keeps status updates aligned with the incident workflow rather than a separate tool. Can we control who sees what, and can customers subscribe? Yes. Rootly supports public, private, and internal pages (with authenticated/SSO access for private pages) and subscriptions so customers and stakeholders opt in to incident and maintenance updates. Pages are fully branded on your own domain. ### Pricing How much does Rootly cost? Incident Response is $20 and On-Call is $20 per user per month, or $40 for both at list price. AI SRE requires Incident Response and is priced on request, Enterprise pricing is custom, and the free trial typically lasts two weeks. Full detail: https://rootly.com/pricing Is On-Call included in the Incident Response price? No. On-Call is a separate product at $20 per user per month. Incident Response and On-Call each work on their own, and together they cost $40 per user per month at list price. ### Security & Data Does Rootly train on my incident data? No. Rootly enforces zero third-party model training. Your incident data is used exclusively for your organization - never pooled with other customers, never used to train general models. Sensitive data (links, emails, passwords) is redacted before being sent to AI providers, and teams can bring their own LLM keys. Where is incident data stored, and who can access it? Access mirrors your org's permissions (SSO/RBAC) and is auditable - only the right people can view or edit sensitive incident artifacts, and AI access respects incident privacy settings. ### Comparisons & Migration Does Rootly replace PagerDuty, and is it more affordable? Yes. PagerDuty notifies a person when something breaks; Rootly handles the page and everything after - coordination, resolution, communication, and learning - in one platform. PagerDuty gates core capabilities behind add-ons (AIOps, Advance, Status Pages); Rootly includes them on every plan, typically at about half the total cost, with a free tier for non-pageable stakeholders. How is Rootly different from incident.io or Resolve AI? Rootly and incident.io are close in shape. Each runs its AI SRE inside an incident management platform that also handles on-call and status pages, attaches confidence scores and evidence to its findings, and leaves changes to a person. The clearest difference is the starting point. Rootly AI SRE starts investigating the moment an alert fires, and incident.io's Investigations start once an incident is declared. Resolve AI is an AI SRE platform built to take first response on alerts and resolve incidents autonomously. Rootly AI SRE instead sits inside the incident workflow responders already run, with on-call, incident channels, status pages, and retrospectives in one platform, and never auto-remediates without human sign-off. It also enforces zero third-party training on your data. When is Opsgenie shutting down, and what are the options? Atlassian closed new Opsgenie sales in June 2025 and set end-of-life for April 2027, after which the platform becomes inaccessible and unexported data is deleted. Rootly's on-call concepts closely mirror Opsgenie. The Rootly-led import brings over users, teams and their routing rules, schedules with rotations and overrides, services, and notification rules, and the Rootly team covers escalation policies with you during scoping. You can keep using Jira, Confluence, and other Atlassian tools. Can Jira Service Management do incident management? JSM logs incidents as tickets and, since absorbing Opsgenie, includes basic on-call. But ownership-based paging, live response coordination, automated comms, root-cause investigation, and retrospectives are manual in JSM. Rootly runs each natively; most teams keep JSM for ITSM/service-desk and use Rootly to run incidents. Does Rootly help with migration, and how long does it take? Yes. Rootly runs a read-only audit of your current setup, delivers a findings report, and executes the migration with a tailored plan. Most teams are live within a week (large enterprise tenants 6 weeks to 3 months), and Rootly will buy out your remaining PagerDuty contract - you don't start paying until it ends.