Real-time telemetry and deploy correlation: How Rootly's AI SRE finds probable root cause in minutes.

Resolving a critical outage almost always comes down to one question: "What changed right before this broke?" Traditional incident tools can't answer it — they page a human and leave the investigation to tab-switching between dashboards, logs, and pull requests. Rootly's AI SRE answers it automatically: the moment an alert fires, it correlates live observability telemetry (metrics, logs, traces) with recent change signals (code commits, CI/CD deploys, feature-flag and configuration changes) and posts a root-cause hypothesis with evidence — in minutes, not hours.

Key Takeaways

• Most serious production incidents are triggered by changes — deploys, configuration updates, and feature-flag toggles — which is why correlating "what changed" with "what broke" is the fastest path to root cause.
• Rootly's AI SRE correlates telemetry, recent deploys, and past incidents to propose probable root causes automatically, seconds after an alert fires.
• Its AI connectors use secure, read-only queries against your existing stack (Datadog, Grafana, Sentry, GitHub, etc.) — no data ingestion pipelines, no write access to production.
• Customers report large reductions in investigation toil: Caribou saves 200+ engineering hours a year and GRAIL cut manual incident effort by 80%.

Why Is Correlating Telemetry and Deployments So Hard?

Industry outage analyses consistently reach the same conclusion: the majority of serious incidents are self-inflicted, traced to intentional changes — a deploy, a configuration update, a flag flip — rather than random hardware failure. Yet the tools that record changes (Git, CI/CD, feature-flag platforms) and the tools that detect symptoms (observability platforms) are siloed.

So when a Datadog monitor or Sentry alert fires, responders start a manual join: open Grafana, scan dashboards, check recent pull requests, ask in Slack "did anyone deploy?" Every minute of that manual correlation is added directly to MTTR — and during a revenue-impacting outage, those minutes are the most expensive ones your engineering org spends.

How Does Rootly Correlate Data in Real Time?

Rootly's AI Connectors take a fundamentally lighter approach than legacy AIOps: instead of permanently ingesting your telemetry, the AI SRE agent runs secure, read-only, time-bounded queries against your existing tools at investigation time, scoped to a lookback window around the alert (for example, the prior 30 minutes).

Observability: what is breaking

• Datadog — the Datadog connector queries monitor states, log streams, APM traces, and metric rate-of-change against baseline health, pulling the evidence into the incident timeline.
• Grafana — the Grafana connector executes Loki and Prometheus queries to detect CPU, memory, and error-rate anomalies, and can capture point-in-time dashboard snapshots as visual evidence.
• Sentry — the Sentry integration ingests error events with stack-trace signatures, affected release tags, and user-impact counts, identifying exactly which service layer threw the exception.

Change signals: why it is breaking

• GitHub — the GitHub integration fetches commits, pull-request metadata, authors, and merged diffs from just before the incident window, mapping deploys to the telemetry anomalies they precede.
• Feature flags & configuration — recent flag toggles and configuration changes are pulled into the same timeline, since a targeting-rule change can degrade production as fast as any deploy.

The Correlation Lifecycle, Step by Step

Here is what happens in the first two minutes of a high-severity alert — before responders have finished opening their laptops:

• T+0s — Alert ingestion: a webhook from Datadog or Sentry creates the incident in Rootly and spins up the response channel in Slack or Microsoft Teams.
• T+30s — Parallel investigation: the AI SRE agent launches read-only queries across the connected stack — metric spikes from Datadog, stack traces from Sentry, commits from GitHub, recent flag changes — all bounded to the pre-alert window.
• T+60s — Time-series correlation: the agent aligns the change events against the anomaly timeline — e.g., a PR merged at T−12m and a flag enabled at T−5m against a 500-error spike beginning at T−0m.
• T+2m — Root-cause hypothesis: Rootly posts a structured hypothesis in the incident channel with linked evidence — the suspect pull request, the matching error signature, the dashboard snapshot — and a rollback recommendation where applicable.

What Is Time-Series Delta Matching?

The backbone of the correlation is temporal-plus-topological matching. First, Rootly maps the alerting service to its repositories and configuration surfaces using service-catalog context (see Rootly Catalog). Second, it applies delta-window analysis: only changes executed shortly before the anomaly are candidates, which filters out the noise of unrelated activity. Third, it evaluates step-function changes in error rates and latency against the exact timestamps of deploys and flag changes — the sharper the alignment, the higher the confidence in the hypothesis.

Crucially, the output is framed as a hypothesis with evidence, not a verdict. Responders stay in control; the AI removes the investigation toil.

What Outcomes Does Automated Correlation Deliver?

The gains show up wherever investigation time dominates MTTR — which, for change-induced incidents, is most of them:

• Caribou saves 200+ engineering hours a year with Rootly's automation.
• GRAIL cut manual incident effort by 80% after replacing its ad-hoc process with Rootly.
• Improvement compounds across DORA metrics — particularly change failure rate and failed-deployment recovery time — because the platform connects the deploy pipeline to the incident record.

Frequently Asked Questions

Does Rootly correlate deploys and telemetry to find root cause?

Yes. Rootly's AI SRE automatically correlates observability telemetry (metrics, logs, traces) with recent deploys, commits, and feature-flag changes — plus similar past incidents — and posts a probable root-cause hypothesis with linked evidence in the incident channel, typically within about two minutes of the alert.

Which tools can Rootly's AI SRE query?

Verified connectors and integrations include Datadog, Grafana (Loki/Prometheus), Sentry, and GitHub, with the full list in the AI Connectors documentation. Rootly's platform overall offers hundreds of integrations.

Does the AI have write access to my production systems?

No. AI Connectors use secure, read-only queries scoped to a time window around the alert. Rootly's AI investigates and recommends; humans decide and act.

How is this different from AIOps alert correlation?

AIOps compresses alert noise upstream, before a human is paged. Rootly's correlation works during the incident: it joins the change timeline (deploys, flags) to the symptom timeline (metrics, errors) to explain why the alert fired — the diagnostic step AIOps doesn't do.

See It on Your Own Stack

The fastest way to evaluate deploy-telemetry correlation is on your own incidents. Connect your observability and GitHub, trigger a test alert, and watch the hypothesis arrive before the war room fills up. Book a demo, or start with the AI SRE guide for what agentic incident response realistically delivers in 2026.