How do you evaluate and benchmark AI agent performance?

Agent evaluation requires measuring both task completion and process quality. Task metrics include success rate measuring the percentage of tasks completed correctly, accuracy measuring the quality of completed tasks against ground truth, and efficiency measuring the number of steps or LLM calls required. Process metrics include tool selection accuracy measuring whether the agent chose the right tools, reasoning quality assessed through trace analysis, error recovery effectiveness, and cost per successful task completion. Benchmark frameworks include SWE-bench for coding agents, WebArena for web interaction agents, and custom task suites for domain-specific agents. Build evaluation datasets with diverse tasks at varying difficulty levels. Implement automated evaluation where possible using assertion checks on final outputs, combined with human evaluation for subjective quality. Track metrics over time to detect performance degradation from model updates or system changes. Compare agent performance against human baselines to quantify automation value.