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

URL: https://rootly.com/blog/telemetry-deploy-correlation-ai-sre
Author: Iain MacKenzie
Published: 2026-07-24
Updated: 2026-09-14

> Rootly's AI SRE correlates real-time telemetry and deploys to find 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](https://rootly.com/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](https://rootly.com/customers/caribou).

## 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](https://docs.rootly.com/ai/connectors/overview) 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](https://docs.rootly.com/ai/connectors/datadog) 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](https://docs.rootly.com/ai/connectors/grafana-managed) executes Loki and Prometheus queries to detect CPU, memory, and error-rate anomalies, and can capture [point-in-time dashboard snapshots](https://docs.rootly.com/integrations/grafana/grafana) as visual evidence.
- **Sentry** — the [Sentry integration](https://docs.rootly.com/integrations/sentry) 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](https://docs.rootly.com/integrations/github/github) 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](https://rootly.com/ai-sre) 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](https://rootly.com/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](https://rootly.com/customers/caribou) with Rootly's automation.
- Improvement compounds across [DORA metrics](https://dora.dev/guides/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](https://docs.rootly.com/ai/connectors/overview). 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](https://rootly.com/demo), or start with the [AI SRE guide](https://rootly.com/ai-sre-guide) for what agentic incident response realistically delivers in 2026.