SUMMARIZATION

Daily Honeycomb Trace Anomaly Digest to Slack

Every morning, pulls the prior day's slowest and most error-prone distributed traces from Honeycomb, summarizes the top anomalies in plain English with OpenAI.

CategorySummarization
Enginesim
Difficultybeginner
Triggerschedule
Steps4
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule fires at 8am
  • ActionQuery Honeycomb for top latency and error spans (last 24h)Honeycomb
  • ActionSummarize anomalies into ranked plain-English digestOpenAI
  • OutputPost digest to Slack channelSlack

What it does

Runs on a fixed daily schedule and turns yesterday's raw Honeycomb span data into a short, readable digest. It surfaces the traces that regressed most on latency and error rate, explains what changed in plain language, and drops the summary into a Slack channel so the team starts the day knowing where the system hurt.

When to use it

Use it when your services emit traces to Honeycomb but nobody actually reads the dashboards. A once-a-day narrative digest keeps latency creep and error spikes visible without anyone babysitting query screens.

How it works

  1. 1A schedule fires at 8am local time.
  2. 2Honeycomb runs a query over the last 24 hours, returning the slowest endpoints and highest error-rate operations grouped by service.
  3. 3OpenAI condenses the result set into a ranked, plain-English summary highlighting the top three anomalies and likely-affected users.
  4. 4A Slack message posts the digest with each anomaly as a bullet, including p95 latency and error percentage.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HoneycombDistributed traces and queries.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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