AI AGENTS
Honeycomb deploy-correlated slowdown triage
On a schedule, an agent compares post-deploy latency in Honeycomb against the pre-deploy baseline.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled deploy-vs-latency audit run
- ActionRead deploy markers and latency distributionsHoneycomb
- ActionCompute latency delta across each deploy boundaryHoneycomb
- LogicKeep only deploys with regression beyond margin
- ActionMatch deploy SHA to GitLab merge requestGitLab
- OutputOpen GitLab issue tagging the suspect MRGitLab
What it does
Catches latency regressions that slip past static thresholds by correlating them with deploys. Each run, the agent compares current latency to the baseline before the most recent deploy and flags deploys that made things slower — then ties the regression to the specific GitLab merge request that shipped.
When to use it
When your latency drifts gradually and per-alert thresholds miss it, but you suspect specific deploys are the cause. Great for teams shipping many times a day who want a deploy-vs-latency audit without watching dashboards.
How it works
- 1A schedule kicks off the investigation at a fixed interval.
- 2The agent reads recent deploy markers and latency distributions from Honeycomb.
- 3It computes the latency delta across each deploy boundary per service.
- 4A branch passes only deploys where post-deploy latency regressed beyond a margin.
- 5The agent matches the deploy SHA to its GitLab merge request and authors.
- 6It opens a GitLab issue naming the suspect MR, the latency delta, and the affected operation.
Set it up
What you configure once, before turning it on.
- 1Connect HoneycombDistributed traces and queries.
- 2Connect GitLabRepos, MRs, pipelines, registry.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, 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.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.