Towards Outcome-Oriented, Task-Agnostic Evaluation of AI Agents

2025-11-11

AI Summary

Current AI agent evaluation overemphasizes infrastructure-centric metrics (e.g., latency, throughput), failing to capture decision quality, autonomy, and business value. To address this gap, we propose the first cross-domain, task-agnostic, outcome-oriented evaluation framework. It defines 11 generalizable metrics—including goal completion rate, autonomy index, multi-step task resilience, and business impact efficiency—to systematically quantify agent performance across four dimensions: decision-making, adaptation, execution, and value creation. We conduct large-scale simulation-based comparative experiments across five domains—healthcare, finance, marketing, law, and customer service—using ReAct, chain-of-thought, tool-augmented, and hybrid architectures. Results demonstrate that hybrid architectures achieve superior overall performance, attaining an average goal completion rate of 88.8% and the highest return on investment. The framework thus establishes a rigorous, measurable, and practically applicable standard for evaluating intelligent agents’ real-world efficacy.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As AI agents proliferate across industries and applications, evaluating their performance based solely on infrastructural metrics such as latency, time-to-first-token, or token throughput is proving insufficient. These metrics fail to capture the quality of an agent's decisions, its operational autonomy, or its ultimate business value. This white paper proposes a novel, comprehensive framework of eleven outcome-based, task-agnostic performance metrics for AI agents that transcend domain boundaries. These metrics are designed to enable organizations to evaluate agents based on the quality of their decisions, their degree of autonomy, their adaptability to new challenges, and the tangible business value they deliver, regardless of the underlying model architecture or specific use case. We introduce metrics such as Goal Completion Rate (GCR), Autonomy Index (AIx), Multi-Step Task Resilience (MTR), and Business Impact Efficiency (BIE). Through a large-scale simulated experiment involving four distinct agent architectures (ReAct, Chain-of-Thought, Tool-Augmented, Hybrid) across five diverse domains (Healthcare, Finance, Marketing, Legal, and Customer Service), we demonstrate the framework's efficacy. Our results reveal significant performance trade-offs between different agent designs, highlighting the Hybrid Agent as the most consistently high-performing model across the majority of our proposed metrics, achieving an average Goal Completion Rate of 88.8% and the highest Return on Investment (ROI). This work provides a robust, standardized methodology for the holistic evaluation of AI agents, paving the way for more effective development, deployment, and governance.

Problem

• Proposing outcome-based metrics to evaluate AI agent decision quality and business value
• Developing task-agnostic evaluation framework for AI agent autonomy and adaptability
• Establishing standardized methodology for cross-domain AI agent performance assessment

Innovation

• Proposes outcome-based task-agnostic performance metrics framework
• Introduces Goal Completion Rate and Autonomy Index metrics
• Demonstrates Hybrid Agent achieves highest performance consistently