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Improvement doesn’t end at launch. Run experiments against curated datasets to prove a change is genuinely better, evaluate production traffic as it flows, configure monitors that surface problems early, and export traces to a coding agent that turns failures into fixes.

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Turn failing traces into a better agent | Ep. 10

Curate a dataset of test cases and run experiments to prove a change is a genuine improvement.

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Run LLM evals on live production traffic with your agents | Ep. 11

Run evaluations automatically against live production traffic to catch regressions as they occur.

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Catch AI agent failures fast with monitors and alerts | Ep. 12

Configure monitors and alerts so you detect problems before your users do.

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Build self-improving AI agents with coding agents | Ep. 13

Send traces to a coding agent to turn observed failures directly into fixes.

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AI agent mastery

A deep dive on building, observing, evaluating, and improving production agents.