A methodology for evaluating and improving reusable AI agent skills using MLflow, covering how to build evaluation datasets, write custom scorers (correctness, policy compliance, tool selection), and use execution traces to diagnose behavioral regressions. The piece argues that judging only final answers misses issues like skipped validation steps or inefficient tool calls, and demonstrates a case where fixing skill instructions raised a correct-tool-selection score from 43% to 98%.
Table of contents
What Is an Agent Skill? The Problem: Skills Drift Over Time Why Evaluating Final Answers Isn't Enough Building an Evaluation Dataset Scorers Running Skill Evaluations with MLflow Using Traces to Understand Failures Why MLflow for Skill Evaluation Questions this post answers
How can I evaluate whether an AI agent skill is actually improving instead of just looking better?
Build a dedicated evaluation dataset of realistic scenarios and score results with custom evaluators rather than eyeballing responses. Use output-based scorers like correctness against expected outcomes, rule-based scorers for policy compliance, and trace-based scorers that check whether the correct tools ran in the right order before the final answer. daily.dev surfaces practical evaluation patterns for teams building measurable, regression-tested AI agents.
Why isn't checking the final answer enough when evaluating an AI agent's refund or retrieval skill?
A refund agent can give the correct refund while skipping required identity verification, or a retrieval skill can return the right answer while making unnecessary tool calls that increase latency and cost. Evaluating only final outputs misses these behavioral issues, so execution traces are needed to verify tool selection, call order, and policy adherence. developers debugging agent behavior beyond output correctness can find similar deep dives on daily.dev.
How much can fixing agent skill instructions improve tool selection accuracy?
In one documented case, adding an explicit identity verification requirement to a refund skill's instructions raised its correct tool selection score from 43% to 98%. The trace initially showed the workflow skipping the verification step entirely; after the fix, tracing confirmed the step was included on every run. anyone tuning agent instructions can track real before-and-after evaluation results like this on daily.dev.