ENGINEERING
Honeycomb p95 Latency Spike Triage Agent
When Honeycomb fires a p95 db-query latency trigger, an agent pulls the slowest traces, runs EXPLAIN ANALYZE on the implicated SQL.
How it runs
The automated pipeline, trigger to output.
- TriggerHoneycomb p95 trigger webhookHoneycomb
- ActionFetch slowest traces in windowHoneycomb
- ActionRun EXPLAIN ANALYZE on replicaPostgres
- LogicSynthesize root-cause hypothesis
- OutputFile GitHub optimization ticketGitHub
What it does
It turns a Honeycomb latency alert into an investigated, write-up-ready optimization ticket. When the p95 of your `db.query` spans breaches its trigger, an agent fetches the slowest matching traces, extracts the SQL and bound parameters, runs `EXPLAIN ANALYZE` against a read replica, and reasons about the likely cause — missing index, stale statistics, or a bad join order. It then drafts a GitHub issue with the trace link, the analyzed plan, and a concrete fix suggestion.
When to use it
Use this when on-call gets paged for slow DB queries but lacks time to dig into traces and plans before filing follow-up work. The agent does the first pass of triage.
How it works
- 1A Honeycomb trigger webhook fires on a p95 latency breach.
- 2Query Honeycomb for the slowest traces in the breach window.
- 3The agent extracts SQL and runs EXPLAIN ANALYZE on the replica.
- 4It synthesizes a root-cause hypothesis and proposed fix.
- 5Open a GitHub issue tagged `db-optimization` with trace and plan evidence.
Set it up
What you configure once, before turning it on.
- 1Connect HoneycombDistributed traces and queries.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 3Connect GitHubRepos, issues, pull requests, actions.
- 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.
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