What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment
This provides a systematic evaluation method for AI agents, addressing a key challenge in agent development, though it is incremental as it builds on existing evaluation paradigms.
The authors tackled the problem of evaluating AI agents by introducing the Agent GPA framework, which assesses goal-plan-action alignment through five metrics, and demonstrated its effectiveness on benchmark datasets by covering all agent errors on TRAIL/GAIA and achieving up to 95% agreement with human annotations.
We introduce the Agent GPA (Goal-Plan-Action) framework: an evaluation paradigm based on an agent's operational loop of setting goals, devising plans, and executing actions. The framework includes five evaluation metrics: Goal Fulfillment, Logical Consistency, Execution Efficiency, Plan Quality, and Plan Adherence. Logical Consistency checks that an agent's actions are consistent with its prior actions. Execution Efficiency checks whether the agent executes in the most efficient way to achieve its goal. Plan Quality checks whether an agent's plans are aligned with its goals; Plan Adherence checks if an agent's actions are aligned with its plan; and Goal Fulfillment checks that agent's final outcomes match the stated goals. Our experimental results on two benchmark datasets - the public TRAIL/GAIA dataset and an internal dataset for a production-grade data agent - show that this framework (a) provides a systematic way to cover a broad range of agent failures, including all agent errors on the TRAIL/GAIA benchmark dataset; (b) supports LLM-judges that exhibit strong agreement with human annotation, covering 80% to over 95% errors; and (c) localizes errors with 86% agreement to enable targeted improvement of agent performance.