ENGINEERING
AI root-cause draft: agent investigates a slow trace across Honeycomb + Sentry and drafts a GitLab issue
On a latency-budget breach, an investigative agent gathers the Honeycomb span breakdown and the concurrent Sentry errors, reasons about the likely root cause.
How it runs
The automated pipeline, trigger to output.
- TriggerHoneycomb latency-budget breach (webhook)Honeycomb
- ActionAgent retrieves full span breakdown from HoneycombHoneycomb
- ActionAgent fetches correlated Sentry errors + stack tracesSentry
- LogicAgent reasons to a ranked root-cause hypothesis
- ActionDraft structured GitLab issue with hypothesis + evidenceGitLab
- OutputNotify owning team in Slack with issue linkSlack
What it does
Does the first pass of root-cause analysis for you. Instead of a static join, an agent reads the slow trace's span breakdown, the correlated Sentry stack traces, and recent context, then writes a GitLab issue containing a plain-language hypothesis, the supporting evidence, and concrete next steps — leaving a human to confirm or correct.
When to use it
Use when latency regressions are nuanced and a raw error dump isn't enough to point at a cause. Best for teams who want a thoughtful starting hypothesis on the ticket, not just linked artifacts, and are comfortable reviewing agent-drafted analysis.
How it works
- 1A Honeycomb trigger fires on a trace that breaches its latency budget.
- 2The agent retrieves the full span breakdown for the slow trace from Honeycomb.
- 3It fetches the correlated Sentry errors and stack traces for the same service and window.
- 4The agent reasons over the combined signals to form a ranked root-cause hypothesis and next steps.
- 5It drafts a structured GitLab issue with the hypothesis, evidence links, and a confidence note for human review.
Set it up
What you configure once, before turning it on.
- 1Connect HoneycombDistributed traces and queries.
- 2Connect SentryErrors, performance, releases.
- 3Connect GitLabRepos, MRs, pipelines, registry.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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