How to Evaluate AI Agents on Process and Tool Use, Not Just Final Output

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A software developer has published an open-source framework for evaluating AI agents by assessing their step-by-step trajectories rather than only their final answers. Unlike standard LLM evaluation, which grades a single text output, agentic systems involve multi-step reasoning and tool calls where early errors can silently corrupt results even when the final response appears correct. To test this approach, the author built a bug-fixing agent, deliberately introduced 10 bugs, and constructed a custom evaluation harness around it. The framework distinguishes between capability evaluations, which probe the limits of an agent's abilities, and regression evaluations, which guard against breakage when prompts or models are updated. The author argues that generic benchmarks like MMLU or SWE-bench measure base model intelligence but cannot assess whether a custom agent wired to specific tools is performing reliably.

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