hiddink-harness:agent-eval-framework

AI agent efficiency benchmarking uses a 4-metric framework to measure correctness, step ratios, tool call efficiency, and latency against ideal trajectory baselines. This identifies redundant reasoning or unnecessary tool usage during agent development.

Efficiency regression testing runs the agent-eval-framework gate command followed by the agent name to perform a full evaluation. This two-phase gating workflow checks correctness thresholds first, then evaluates efficiency to ensure updates do not degrade performance.

Quantitative trajectory-based evaluation measures correctness, step ratios, tool call ratios, and latency. These four metrics compare actual agent performance against ideal performance baselines to quantify resource utilization and effectiveness.

Episodic memory and artifact storage are required to track and compare agent performance across multiple sessions. This integration enables the framework to measure trajectory-based efficiency and maintain historical benchmarking data.

Phased gating for AI agent deployment should be used when you need to ensure only high-quality agents are promoted. It requires correctness thresholds to be met before evaluating efficiency metrics, preventing inefficient agents from passing evaluation.

The best way to compare two AI agent versions is using a quantitative evaluation framework to benchmark code quality and resource utilization. This ensures new updates improve correctness without increasing unnecessary tool calls or reasoning steps.