AI AGENTS
Post-Deploy Honeycomb Regression to Load-Test Gate Agent
After a GitHub deploy, the agent compares Honeycomb performance before and after the release; if a span regresses, it proposes a load-test plan and files it as a blocking check…
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub deployment_status webhook firesGitHub
- ActionQuery Honeycomb pre/post-deploy metric windowsHoneycomb
- LogicBranch on regression threshold breach
- ActionAgent authors load-test plan for suspect endpoint
- ActionComment plan and label the deploying PRGitHub
- OutputNotify PR author in Slack with chartSlack
What it does
Watches for a deploy event, then asks Honeycomb whether the new build moved key latency or error metrics in the wrong direction. When it detects a regression, it correlates the change to the deployed PR and writes a load-test plan designed to reproduce the regression in a controlled run.
When to use it
Use it as a safety net for services where canary metrics are noisy and a slow regression slips past synthetic checks. Ideal for teams practicing continuous deploy who want an automated second opinion tied directly to the PR that shipped.
How it works
- 1A GitHub deployment_status webhook fires when a release reaches production.
- 2The agent queries Honeycomb for the same heartbeat metric across the pre- and post-deploy windows.
- 3It branches on whether any span breached the regression threshold; clean deploys exit quietly.
- 4On a regression, the agent identifies the suspect endpoint and authors a load-test plan with ramp profile and pass/fail thresholds.
- 5It posts the plan as a PR comment on the deploying GitHub PR and applies a needs-loadtest label.
- 6It pings the author in Slack with the regression chart link and the proposed experiment.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect HoneycombDistributed traces and queries.
- 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.
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