AI AGENTS
Honeycomb High-Cardinality Hunter with GitHub PR Proposals
Weekly agent that scans Honeycomb datasets for the columns driving cardinality blowup, then opens a GitHub PR proposing drop or aggregate rules for the offending fields.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires
- ActionQuery Honeycomb column cardinality and volumeHoneycomb
- LogicRank columns and decide drop vs aggregate
- ActionRead collector config and draft rule edits from repoGitHub
- ActionOpen GitHub PR with diff and projected savingsGitHub
- OutputPost PR link to Slack for reviewSlack
What it does
Each week this agent inspects your Honeycomb datasets, finds the high-cardinality columns (request IDs, full URLs, raw user agents) that inflate your event volume and bill, and turns the worst offenders into a concrete, reviewable change. Instead of a dashboard nobody reads, you get a GitHub pull request editing your collector config with the exact drop/aggregate rules and an estimated volume reduction.
When to use it
Use it when your Honeycomb spend keeps climbing and you suspect a handful of unbounded attributes are responsible. Good for platform and observability teams who manage telemetry pipeline config in git and want changes proposed as PRs rather than applied blindly.
How it works
- 1A weekly schedule fires the run.
- 2The agent queries the Honeycomb API for column cardinality and event counts across each dataset.
- 3It ranks columns by cardinality-times-volume and decides which warrant a drop versus a low-cardinality aggregate.
- 4It reads the current collector config from the repo, drafts the rule edits, and estimates the resulting volume cut.
- 5It opens a GitHub PR with the diff, the per-column rationale, and projected savings, then posts the PR link to Slack for review.
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.
More AI Agents workflows
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A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
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When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
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.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.