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.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerwebhook
Steps6
Setup~15 min

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 issueGitLabGitLab

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

  1. 1A webhook arrives carrying a service name and a time window.
  2. 2The agent queries Honeycomb for the slow traces in that exact window.
  3. 3It pivots spans by operation and duration to locate the bottleneck.
  4. 4A branch checks whether the data is sufficient or the window needs widening.
  5. 5The agent writes an investigation narrative and a ranked hypothesis.
  6. 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.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect HoneycombDistributed traces and queries.
  3. 3
    Connect GitLabRepos, MRs, pipelines, registry.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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