What are the evaluation frameworks for measuring AI agent performance?

AI agent evaluation requires metrics across multiple dimensions: task completion rate (percentage of tasks successfully completed end-to-end), step efficiency (number of steps taken vs optimal path), tool selection accuracy (did the agent choose the right tools?), reasoning quality (were intermediate decisions sound?), and user satisfaction. Evaluation approaches: offline evaluation using curated test suites with known answers and expected tool call sequences, online evaluation using production traffic with human ratings, and automated evaluation using LLM-as-judge to assess reasoning quality. Key metrics: success rate (task completed correctly), efficiency (steps/tokens/time per task), cost (LLM API cost per task), safety (rate of policy violations, harmful outputs), and robustness (performance across paraphrased or adversarial inputs). Framework implementation: define a test suite of 200+ diverse tasks, run each task through the agent, compare outputs against gold standards, and compute aggregate metrics. Track metrics over time to detect regressions. Specialized benchmarks: GAIA for general AI assistants, AgentBench for tool-using agents, WebArena for web browsing agents, and SWE-bench for software engineering agents. Best practice: maintain both automated and human evaluation pipelines, as automated metrics can miss subtle quality issues.