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Endigest AI Core Summary
This post introduces coSTAR, Databricks' methodology for reliably testing and iterating on AI agents using MLflow.
•coSTAR runs two coupled STAR loops: an agent loop that uses judges to auto-score traces and refine the agent, and a judge loop that aligns judges with human expert assessments.
•Scenario definitions act as test fixtures, bundling initial state, user prompts, and expected outcomes in a portable, reusable structure.
•Trace capture decouples execution from scoring, allowing judges to be re-run against persisted traces without repeating expensive agent runs.
•Judges are implemented as agentic LLMs equipped with tools to selectively inspect traces, avoiding the quality degradation of feeding full traces into a single context window.
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The test suite spans three categories: deterministic checks (syntax, schema, tool sequence), LLM-based judgment (code quality, best practices), and operational metrics (token usage, latency, tool call failure rates).
This summary was automatically generated by AI based on the original article and may not be fully accurate.
Databricks
Google Cloud
Stripe