AI Agent Evaluation — Test Reliability Before Production

I will build an evaluation framework for your AI agent — covering task success rate measurement, step efficiency analysis, tool call accuracy, error recovery assessment, and an adversarial test suite for edge cases and failure modes — giving you quantitative evidence of agent reliability before deploying to production. Agent evaluation is fundamentally different from model evaluation — agents produce different outputs on repeated runs of the same input due to LLM non-determinism and tool output variation, requiring statistical evaluation across many runs rather than a single deterministic test.

The framework covers task success rate evaluation across 30+ test cases, step count efficiency analysis, tool call correctness scoring, error recovery evaluation, adversarial test cases for common failure modes, and a reliability report with confidence intervals on key metrics.