What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment

• URL: http://arxiv.org/abs/2510.08847v1
• Date: Thu, 09 Oct 2025 22:40:19 GMT
• Title: What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment
• Authors: Allison Sihan Jia, Daniel Huang, Nikhil Vytla, Nirvika Choudhury, John C Mitchell, Anupam Datta,
• Abstract summary: Agent GPA is an evaluation paradigm based on an agent's operational loop of setting goals, devising plans, and executing actions.<n>The framework includes five evaluation metrics: Goal Fulfillment, Logical Consistency, Execution Efficiency, Plan Quality, and Plan Adherence.
• Score: 3.5583478152586756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• VeRO: An Evaluation Harness for Agents to Optimize Agents [5.227525836910522]
  We introduce VERO (Versioning, Rewards, and Observations), which provides a reproducible evaluation harness with versioned agent snapshots, budget-controlled evaluation, and structured execution traces.<n>We conduct an empirical study comparing target agents and analyzing which modifications reliably improve target agent performance.
  arXiv  Detail & Related papers  (2026-02-25T23:40:22Z)
• POLARIS: Typed Planning and Governed Execution for Agentic AI in Back-Office Automation [0.28055179094637683]
  POLARIS is a governed orchestration framework that treats automation as typed plan synthesis and validated execution over LLM agents.<n> Empirically, POLARIS achieves a micro F1 of 0.81 on the SROIE dataset and, on a controlled synthetic suite, achieves 0.95 to 1.00 precision for anomaly routing with preserved audit trails.
  arXiv  Detail & Related papers  (2026-01-16T22:38:21Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• Randomized Controlled Trials for Conditional Access Optimization Agent [0.9558392439655014]
  We report results from the first randomized controlled trial (RCT) evaluating an AI agent for Conditional Access (CA) policy management in Microsoft Entra.<n>Agent access produced substantial gains: accuracy improved by 48% and task completion time decreased by 43% while holding accuracy constant.<n>These findings demonstrate that purpose-built AI agents can significantly enhance both speed and accuracy in identity administration.
  arXiv  Detail & Related papers  (2025-11-17T19:33:03Z)
• AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress [71.02263260394261]
  Large language models (LLMs) still encounter challenges in multi-turn decision-making tasks.<n>We build process reward models (PRMs) to evaluate each decision and guide the agent's decision-making process.<n>AgentPRM captures both the interdependence between sequential decisions and their contribution to the final goal.
  arXiv  Detail & Related papers  (2025-11-11T14:57:54Z)
• Alita-G: Self-Evolving Generative Agent for Agent Generation [54.49365835457433]
  We present ALITA-G, a framework that transforms a general-purpose agent into a domain expert.<n>In this framework, a generalist agent executes a curated suite of target-domain tasks.<n>It attains strong gains while reducing computation costs.
  arXiv  Detail & Related papers  (2025-10-27T17:59:14Z)
• How can we assess human-agent interactions? Case studies in software agent design [52.953425368394306]
  We make two major steps towards the rigorous assessment of human-agent interactions.<n>We propose PULSE, a framework for more efficient human-centric evaluation of agent designs.<n>We deploy the framework on a large-scale web platform built around the open-source software agent OpenHands.
  arXiv  Detail & Related papers  (2025-10-10T19:04:28Z)
• Where LLM Agents Fail and How They can Learn From Failures [62.196870049524364]
  Large Language Model (LLM) agents have shown promise in solving complex, multi-step tasks.<n>They amplify vulnerability to cascading failures, where a single root-cause error propagates through subsequent decisions.<n>Current systems lack a framework that can comprehensively understand agent error in a modular and systemic way.<n>We introduce the AgentErrorTaxonomy, a modular classification of failure modes spanning memory, reflection, planning, action, and system-level operations.
  arXiv  Detail & Related papers  (2025-09-29T18:20:27Z)
• CORE: Full-Path Evaluation of LLM Agents Beyond Final State [2.0391237204597368]
  Existing agentic benchmarks often reduce evaluation to a binary judgment of the final state.<n>We propose a framework based on deterministic finite automata that encodes tasks as sets of valid tool-use paths.<n>We introduce CORE, a suite of five metrics, namely Path Correctness, Path Correctness - Kendall's tau Composite, Prefix Criticality, Harmful-Call Rate, and Efficiency.
  arXiv  Detail & Related papers  (2025-09-25T10:49:35Z)
• VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection [55.957275374847484]
  VulAgent is a multi-agent vulnerability detection framework based on hypothesis validation.<n>It implements a semantics-sensitive, multi-view detection pipeline, each aligned to a specific analysis perspective.<n>On average, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450%, and reduces the false positive rate by about 36%.
  arXiv  Detail & Related papers  (2025-09-15T02:25:38Z)
• Auto-Eval Judge: Towards a General Agentic Framework for Task Completion Evaluation [4.08768677009363]
  We propose a generalizable, modular framework for evaluating agent task completion independent of the task domain.<n>We validate our framework by evaluating the Magentic-One Actor Agent on two benchmarks, GAIA and BigCodeBench.<n>Our Judge Agent predicts task success with closer agreement to human evaluations, achieving 4.76% and 10.52% higher alignment accuracy, respectively.
  arXiv  Detail & Related papers  (2025-08-07T15:39:48Z)
• PIPA: A Unified Evaluation Protocol for Diagnosing Interactive Planning Agents [12.052972947563424]
  Existing benchmarks predominantly evaluate agent performance based on task completion as a proxy for overall effectiveness.<n>We propose PIPA, a unified evaluation protocol that conceptualizes the behavioral process of interactive task planning agents.<n>Our analyses show that agents excel in different behavioral stages, with user satisfaction shaped by both outcomes and intermediate behaviors.
  arXiv  Detail & Related papers  (2025-05-02T21:27:10Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems.<n>In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy.<n> Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• Learning to Use Tools via Cooperative and Interactive Agents [58.77710337157665]
  Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. We propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately. Our experiments on three datasets show that the LLMs, when equipped with ConAgents, outperform baselines with substantial improvement.
  arXiv  Detail & Related papers  (2024-03-05T15:08:16Z)
• Fairness in Multi-Agent Planning [2.7184224088243356]
  This paper adapts well-known fairness schemes to Multi-Agent Planning (MAP) It introduces two novel approaches to generate cost-aware fair plans. Empirical results in several standard MAP benchmarks show that these approaches outperform different baselines.
  arXiv  Detail & Related papers  (2022-12-01T13:58:46Z)
• Formalizing the Problem of Side Effect Regularization [81.97441214404247]
  We propose a formal criterion for side effect regularization via the assistance game framework. In these games, the agent solves a partially observable Markov decision process. We show that this POMDP is solved by trading off the proxy reward with the agent's ability to achieve a range of future tasks.
  arXiv  Detail & Related papers  (2022-06-23T16:36:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.