AI AGENTS
Stage remediation steps from a Sentry error spike for human approval
When Sentry flags a new high-volume issue, an agent pulls the stack trace plus correlated Datadog metrics, drafts a ranked remediation plan.
How it runs
The automated pipeline, trigger to output.
- TriggerSentry issue crosses event-rate thresholdSentry
- ActionFetch full issue, stack trace, and releaseSentry
- ActionPull correlated Datadog metrics and deploy markersDatadog
- LogicRank remediations, drop options with no rollback
- OutputPost staged plan to Slack with Approve/RejectSlack
What it does
Turns a noisy Sentry alert into a concrete, reviewable action plan. The agent reads the failing issue, enriches it with host and service metrics from Datadog, and proposes the most likely fixes (rollback, feature-flag kill, config revert) ranked by confidence. Nothing executes — every step waits in Slack for a human to approve.
When to use it
Use it for production services where a single Sentry issue can spike to thousands of events fast and your on-call rotation needs a starting point at 3am, not a blank incident channel. Best when you want speed of triage without giving an agent unsupervised write access to prod.
How it works
- 1A Sentry issue alert fires when an issue crosses an event-rate threshold.
- 2The agent fetches the full issue: stack trace, release, affected users, and breadcrumb timeline.
- 3It queries Datadog for the matching service's error rate, latency, and recent deploy markers around the spike window.
- 4Decision logic ranks candidate remediations and filters out any that lack a safe rollback path.
- 5The staged plan posts to the on-call Slack channel with Approve and Reject actions and a link back to the Sentry issue.
Set it up
What you configure once, before turning it on.
- 1Connect SentryErrors, performance, releases.
- 2Connect DatadogMetrics, traces, log search.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI Agents workflows
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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