SUMMARIZATION

Axiom + Honeycomb Cross-Source Reliability Brief

Pulls the week's top error patterns from Axiom and matching latency/trace spikes from Honeycomb, correlates them, and writes a single brief that explains which log patterns…

CategorySummarization
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionFetch top Axiom error signaturesAxiom
  • ActionFetch Honeycomb latency + slow-trace spikesHoneycomb
  • LogicCorrelate errors to performance impact
  • ActionWrite correlated reliability briefOpenAI
  • OutputSave dated page to NotionNotionNotion

What it does

Most reliability summaries report logs and traces separately, leaving humans to guess which noisy error actually slowed users down. This template joins both: it takes Axiom's top error patterns and Honeycomb's latency outliers for the same week, lines them up by service and time window, and produces one brief that says which log patterns coincided with real performance pain — and which were harmless chatter.

When to use it

Use it when you run Axiom for logs and Honeycomb for tracing and want a weekly verdict on what mattered, not two dashboards to reconcile by hand.

How it works

  1. 1A weekly schedule trigger kicks off the run.
  2. 2An Axiom action returns the top error signatures with counts.
  3. 3A Honeycomb action returns p95 latency and slow-trace spikes per service for the same window.
  4. 4A logic step correlates the two sets by service and overlapping time, tagging each error pattern as user-impacting or benign.
  5. 5An OpenAI action drafts a correlated reliability brief in plain English.
  6. 6A Notion output saves it as a dated page in the reliability log.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AxiomLog streams, queries, dashboards.
  2. 2
    Connect HoneycombDistributed traces and queries.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect NotionPages, databases, comments.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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