AI AGENTS
On-demand Honeycomb latency investigation via webhook
A webhook with a service name and time window kicks off an agent that pivots the relevant Honeycomb traces, builds a hypothesis.
How it runs
The automated pipeline, trigger to output.
- TriggerWebhook with service name and time windowHTTP webhook
- ActionQuery Honeycomb slow traces in the windowHoneycomb
- ActionPivot spans by operation and durationHoneycomb
- LogicCheck data sufficiency or widen window
- ActionAgent writes investigation and hypothesis
- OutputReturn findings and draft GitLab issueGitLab
What it does
Lets anyone launch a latency investigation on demand by hitting a webhook with a service and time window — from a chatops command, a runbook button, or an incident tool. The agent does the Honeycomb pivoting and returns a written investigation along with a GitLab issue draft ready to file.
When to use it
When an engineer suspects a slowdown and wants an instant investigation without manually slicing traces, or when an incident tool should auto-launch a probe. Best as a reusable building block other workflows or humans can call.
How it works
- 1A webhook arrives carrying a service name and a time window.
- 2The agent queries Honeycomb for the slow traces in that exact window.
- 3It pivots spans by operation and duration to locate the bottleneck.
- 4A branch checks whether the data is sufficient or the window needs widening.
- 5The agent writes an investigation narrative and a ranked hypothesis.
- 6It returns the findings as a response and drafts a GitLab issue ready to file.
Set it up
What you configure once, before turning it on.
- 1Connect HTTP webhookTrigger any URL on agent actions.
- 2Connect HoneycombDistributed traces and queries.
- 3Connect GitLabRepos, MRs, pipelines, registry.
- 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
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Sentry-to-Confluence Runbook Updater
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.
