AI AGENTS
Honeycomb Anomaly to Load-Test PR Scaffold Agent
On a Honeycomb anomaly, the agent forms a hypothesis and opens a GitHub pull request scaffolding a k6 load-test script targeting the suspect endpoint.
How it runs
The automated pipeline, trigger to output.
- TriggerHoneycomb anomaly trigger firesHoneycomb
- ActionFetch trace and pinpoint suspect endpointHoneycomb
- LogicBranch on whether a test already covers it
- ActionAgent generates parameterized load-test script
- ActionOpen GitHub PR with script and hypothesisGitHub
- OutputNotify owning team in Slack with PR linkSlack
What it does
Goes one step past planning and produces a runnable artifact. After reasoning about the anomalous Honeycomb trace, the agent generates a parameterized load-test script aimed at the suspect endpoint and opens a GitHub PR containing the script plus a hypothesis written into the description — so the experiment is a click away from executing in CI.
When to use it
Use it when your load tests live as code in the repo and you want the agent to remove the boilerplate gap between hypothesis and a runnable test. Best for teams with a CI load-test stage already wired up.
How it works
- 1A Honeycomb anomaly trigger fires with the offending trace.
- 2The agent fetches the trace and pinpoints the endpoint and the failing condition.
- 3It branches on whether an existing test already covers the endpoint to avoid duplicates.
- 4The agent generates a parameterized load-test script with the ramp and assertions matching its hypothesis.
- 5It opens a GitHub PR on a new branch with the script and the hypothesis in the body.
- 6It notifies the owning team in Slack with the PR link and a one-line summary of the experiment.
Set it up
What you configure once, before turning it on.
- 1Connect HoneycombDistributed traces and queries.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 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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Run this workflow in your colony.
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