What are the evaluation frameworks for assessing AI agent performance?

AI agent evaluation measures task completion, efficiency, safety, and user experience. Evaluation dimensions: (1) Task success rate: did the agent complete the assigned task correctly? Measure using a benchmark of tasks with known correct outcomes. (2) Efficiency: how many steps/tool calls/tokens did the agent use? Compare against optimal solution paths. (3) Reliability: does the agent consistently succeed across multiple runs of the same task? Measure variance in outcomes. (4) Safety: does the agent stay within defined guardrails? Test with adversarial inputs designed to trigger unsafe behavior. (5) User experience: is the interaction natural and helpful? Measure through user satisfaction surveys and conversation quality metrics. Benchmark suites: SWE-bench (software engineering tasks: apply patches to real GitHub repos), WebArena (web navigation tasks), GAIA (general AI assistant tasks requiring tool use), AgentBench (multi-domain benchmark), and ToolBench (tool-use evaluation). Evaluation methods: automated metrics (task completion binary, step count, cost), LLM-as-judge (have a powerful model score agent interactions on criteria), human evaluation (domain experts rate quality, safety, and helpfulness), and A/B testing (compare agent versions on real user interactions). Implementation: create a test harness that runs agents against reproducible task sets, record all agent actions and decisions, compare against ground truth or human performance, and generate evaluation reports. Regression testing: maintain a suite of representative tasks, run after every agent change, alert on performance degradation, and track metrics over time. Cost-quality trade-off: evaluate agents across different model sizes and configurations, find the cheapest configuration that meets quality thresholds.