AI AGENTS
Post-Deploy Cardinality Regression Guard
On each GitHub deployment, an agent baselines new Honeycomb attribute cardinality against the prior release and blocks or comments on the PR when a deploy introduces a runaway…
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub deployment event firesGitHub
- ActionSample post-deploy attribute cardinality from HoneycombHoneycomb
- LogicDiff against pre-deploy baseline for runaway fields
- ActionComment warning on the associated GitHub PRGitHub
- OutputPing the author in Slack with suggested capSlack
What it does
Most cost blowups arrive in a deploy that adds an unbounded attribute. This agent runs right after a release: it samples the new attributes flowing into Honeycomb, compares their cardinality to the pre-deploy baseline, and if a freshly introduced field is exploding it comments on the originating GitHub PR with the field name, growth rate, and a recommended cap or drop. It catches regressions while the author still remembers the change.
When to use it
Use it as a guardrail in teams shipping frequently, where a single new label can 10x billable series. Best when deploys are tracked through GitHub deployment events.
How it works
- 1A GitHub deployment event triggers the run.
- 2The agent samples post-deploy attribute cardinality from Honeycomb for the affected services.
- 3A logic step diffs against the stored pre-deploy baseline to find newly introduced or sharply growing fields.
- 4If a runaway field crosses the threshold, it identifies the field and growth multiple.
- 5It posts a warning comment on the associated GitHub PR and pings the author in Slack with a suggested cap.
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.
More AI Agents workflows
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Stale Doc-PR Chaser for Runbook Gaps
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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.
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