AI AGENTS

Honeycomb + Axiom cross-source latency root-cause

On a Honeycomb latency trigger, an agent cross-references the trace window against Axiom application logs to confirm the failing dependency.

CategoryAI Agents
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerHoneycomb latency trigger firesHoneycomb
  • ActionPull slowest spans and time windowHoneycomb
  • ActionQuery Axiom logs for same service and windowAxiom
  • LogicConfirm log evidence supports trace suspect
  • ActionSynthesize root-cause from both sources
  • OutputFile GitLab issue with correlated evidenceGitLabGitLab

What it does

Combines distributed traces with raw logs to produce a higher-confidence root cause. When latency regresses in Honeycomb, the agent pulls the matching log lines from Axiom for the same time window and service, correlates them, and files a GitLab issue backed by evidence from both observability sources.

When to use it

When traces alone tell you where time is spent but not why, and the answer (timeouts, retries, error spikes) lives in your logs. Ideal for teams running Honeycomb for tracing and Axiom for log analytics.

How it works

  1. 1A Honeycomb latency trigger fires for a service.
  2. 2The agent pulls the slowest spans and their time window from Honeycomb.
  3. 3It queries Axiom logs for the same service and window, filtering for errors, retries, and timeouts.
  4. 4A branch confirms whether log evidence supports the trace-level suspect.
  5. 5The agent synthesizes a root-cause statement citing both the trace and the log lines.
  6. 6It opens a GitLab issue with the correlated evidence and links to each source.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HoneycombDistributed traces and queries.
  2. 2
    Connect AxiomLog streams, queries, dashboards.
  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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