Hardware
How to Evaluate AI Agents From Tool Calls to Task Completion
Agent evaluation is moving from scoring individual function calls to judging full task completion within executable environments that track state over multi‑step tool use. Scoring occurs at two levels
Agent evaluation is moving from scoring individual function calls to judging full task completion within executable environments that track state over multi‑step tool use. Scoring occurs at two levels: step‑level process analysis (identifying where a chain fails) and end‑to‑end outcome validity (verifying the final environment matches the goal), with metrics rolled up through a fixed hierarchy of benchmark, trial, task, turn, and step and reported jointly across accuracy, verbosity, and cost. Benchmark comparability focuses on task complexity, statefulness and executable verification rather than reference or LLM‑judge methods; for example, Nemotron 3.5 Lightning achieves 86% accuracy on PinchBench while completing tasks 30 % faster than comparable models, prompting enterprise deployments to use domain‑specific evaluations built from real tickets and APIs gated on environmental state.
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Source: NVIDIA Developer | 2026-09-21