AI evaluation
Evaluate agents by task outcomes, not confident wording
Fluent text is not proof that an agent completed the work. Evaluation should check the artifacts and constraints that matter.
Aryan (@istmX)3 min read
Start with a clear boundary
Define what success produces before changing a model or prompt: a file, a correct update, a reviewed change, or sources that meet stated criteria.
Handle failure deliberately
Build a small repeatable task set with ordinary cases, edge cases, and cases where the agent should stop and ask for information.
Make the result understandable
Track success alongside latency, tool calls, cost, and recovery behavior. Inspect failures by category to find actionable fixes.