Automated service catalogs for incident management: Ownership, dependencies, and runbooks in Rootly.

When an outage starts, the most expensive question is "who owns this?" Production microservices scale faster than any human can track, and responders lose their most critical minutes to identifying affected services, discovering dependencies, and hunting down the owning team. Rootly solves this with an automated, catalog-driven service directory built into the incident lifecycle: when an alert fires, service ownership, dependencies, and runbooks are injected into the response channel automatically — so every incident starts with operational truth instead of an investigation.

Key Takeaways

• An automated service catalog maps services, owners, dependencies, and runbooks — and keeps that map current by syncing from the systems engineers already maintain (Git, Terraform, Backstage, Cortex).
• Rootly's Catalog acts as the routing engine for incident response: alerts are matched to services, pages go to the actual owning team, and context lands in the channel at T+0.
• Catalog data can be managed as code — via an official Terraform provider and an open-source sync CLI — so ownership never drifts from reality.
• Customers report dramatic toil reduction from automated context: GRAIL cut manual incident effort by 80%; Caribou saves 200+ engineering hours a year.

What Is an Automated Service Catalog in Incident Management?

An automated service catalog is a dynamic, centralized repository that maps an organization's software architecture, infrastructure, and team ownership. Unlike static spreadsheets or legacy CMDBs, it stays accurate by syncing with the sources of truth engineers already maintain — CI/CD pipelines, Infrastructure-as-Code, and Internal Developer Portals (IDPs) — so it never suffers configuration drift.

Inside an incident platform, the catalog becomes an intelligent routing engine: it matches incoming alerts to specific services, identifies the on-call team responsible, surfaces upstream and downstream dependencies, and injects the right runbooks directly into the response channel.

Why Service Context Determines MTTR

The first minutes of a high-severity incident are dominated by orientation: which service is degraded, what depends on it, who owns it, and what the known mitigation is. Every one of those questions answered manually inflates MTTR — and pulling in the wrong responders because ownership is ambiguous makes coordination slower still. Industry incident analyses consistently find that attaching explicit service context and role assignment to incidents materially shortens resolution, which matches what Rootly customers see in practice: GRAIL cut manual incident effort by 80% after moving from an ad-hoc process to Rootly's automated workflows.

Core Capabilities of Rootly's Catalog

Custom catalogs and dynamic entity modeling

Rootly's Catalog ships with built-in structures for Services, Teams, Functionalities, Environments, Causes, and Types — and lets platform teams define custom catalogs that model their actual architecture: Domains, Product Areas, Cloud Accounts, Regions, API Gateways. Entities support typed properties and cross-catalog references, so a Service can link directly to its Owning Team and Upstream Services.

Upstream and downstream dependency tracking

Catalog entities carry directional relationships, giving responders instant visibility into upstream impact (which user-facing features degrade when this service fails) and downstream risk (which underlying components might be the true root cause). During alert triage, that context is what separates a root-cause event from a secondary symptom.

Ownership-driven escalation routing

Each service links to primary and secondary owners, Slack channels, and escalation policies. When an alert matches a catalog entity, the page routes to the team that actually owns the component — not a catch-all rotation — and catalog metadata pre-populates the incident form and channel.

How to Manage Catalog Data as Code

Manual catalog upkeep is where most service directories die. Rootly supports git-ops workflows so the catalog tracks reality automatically:

• Open-source sync CLI — rootly-catalog-sync (Go) reconciles external sources into the catalog: local YAML/Jsonnet, GitHub repositories, and Backstage instances, with declarative mapping rules and plan/apply commands for CI pipelines (docs).
• Terraform — the official Rootly Terraform provider exposes hundreds of resources, so services, ownership boundaries, and escalation policies are instantiated alongside the infrastructure they describe (docs).
• IDP bi-directional sync — native Backstage integration (ingest Components, Systems, and APIs while preserving relations, plus in-Backstage incident visibility) and Cortex sync in both directions: entities in, incident metrics back out to service scorecards.

How Catalog Context Drives Automation

The catalog isn't a reference document — it fuels the automation engine. When monitoring webhooks arrive (Datadog, Grafana), Rootly matches alert tags to catalog entities, and workflows can extract any service attribute programmatically to create dedicated channels, page the owning team, and post runbooks. The same context feeds Rootly AI SRE, which uses service-to-repository mapping to correlate telemetry with recent deploys during root-cause investigation.

How Rootly's Catalog Compares

• Architectural focus — Rootly: full incident lifecycle + native AI SRE + custom catalog entities. incident.io: Slack-first response with a built-in catalog. OpsLevel: a dedicated internal developer portal. PagerDuty: alert escalation with basic service mapping.
• Catalog as code — Rootly: official Terraform provider + open-source sync CLI. incident.io: Terraform plus custom code. OpsLevel: Terraform provider. PagerDuty: basic API / Terraform.
• Backstage / Cortex sync — Rootly: bi-directional sync with native Backstage plugins. incident.io: catalog importers. OpsLevel: its own native portal. PagerDuty: webhooks / custom integrations.
• Incident automation from catalog data — Rootly: workflow variables drive channels, paging, and runbooks. incident.io: workflow builder. OpsLevel: scorecards-and-checks focus. PagerDuty: standard notification rules.

Frequently Asked Questions

Does Rootly have a service catalog?

Yes. Rootly includes an automated, catalog-driven service directory — services, teams, environments, and custom entities with typed properties and dependency relationships — that routes alerts to owning teams and injects context into incident channels automatically.

Does Rootly sync with Backstage and Cortex?

Yes, bi-directionally. Backstage Components, Systems, and APIs can be ingested with relations preserved (with native Backstage plugins for in-portal incident visibility), and Cortex entities sync in while incident metrics like MTTR flow back to Cortex scorecards.

Can the catalog be managed as code?

Yes — via the official Rootly Terraform provider and the open-source rootly-catalog-sync CLI, which reconciles YAML/Jsonnet files, GitHub repos, and Backstage instances into the catalog from CI.

How does a service catalog reduce MTTR?

By eliminating orientation time: alerts arrive pre-matched to a service, pages go to the real owner, dependency context shows whether you're looking at cause or symptom, and runbooks are already in the channel. Rootly customers like GRAIL (80% less manual effort) attribute much of their gains to this automated context.

Start With Ownership Truth

If your incident process still begins with "who owns this service?", the catalog is the highest-leverage fix available. See how Rootly connects ownership, dependencies, and response automation on the Rootly Catalog page, explore how it fits a modern incident response stack, or book a demo to map your own architecture into it.