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How to cluster broken agent runs into one issue per failure with Tracely AI

Collapse dozens of failed runs into a single counted issue with a suggested evaluator.

Who it is forA team lead or on-call engineer facing a flood of failed agent runs who needs to know which problem is actually happening.

The problemReading failed runs one by one does not show which failure modes are recurring or which check would have caught them.

The outcomeFailures are grouped by structure and meaning into clusters with counts, each with a suggested evaluator and an option to promote it to a case.

What you need first

  • Docker and Docker Compose
  • For local development: uv, Node 20+ and pnpm
  • A GitHub repository with pull requests, if you want the CI gate
  • Python, to install the tracelyai-sdk package

Steps

  1. Run docker compose --profile demo up -d --build --wait to load the seeded traces and clusters.
  2. Open http://localhost:3001 and go to the clusters view.
  3. Compare cluster counts and open the largest cluster to read its trace examples.
  4. Check the suggested evaluator for that cluster and add it to the trace table if it fits.
  5. Promote the most important cluster to a regression case.

Prompt for your agent

In the Tracely-ai repository, run `docker compose --profile demo up -d --build --wait`, then open the clusters view at http://localhost:3001. List the top three clusters with their counts and suggested evaluators. Verify that the counts add up to the total number of failed traces shown.

Caveat. The demo data is seeded for exploration, so cluster names and counts reflect that sample, not your production agent.

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