Mastering Agentic Techniques: AI Agent Evaluation | NVIDIA Technical Blog

This post introduces a shift in evaluating AI agents from static model benchmarks to dynamic, trajectory-aware metrics. It details five practical tips for evaluating AI agents as production systems: prioritizing task success rate (TSR) over accuracy, evaluating full trajectories (plans, tool calls, reasoning, outcomes) rather than just final answers, making tool usage a first-class signal (measuring selection precision, recall, and schema compliance), scoring reasoning quality and efficiency (soundness, tokens, steps, latency), and building transparent, customizable evaluation from day one. The post emphasizes that reliable agentic systems require tracking outcomes, tool usage, reasoning, and cost together, and wiring those signals into the development process.