Beyond Task Completion: An Assessment Framework for Evaluating Agentic AI Systems

2025-12-14

AI Summary

Existing LLM agent evaluation heavily relies on binary task-completion metrics, overlooking model uncertainty and failing to characterize critical capabilities such as tool invocation, memory management, multi-agent collaboration, and environment interaction. Method: We propose the first end-to-end evaluation framework tailored for multi-agent systems, establishing a “LLM capability–memory–tool–environment” four-pillar paradigm. It systematically models and quantifies runtime non-deterministic behavioral deviations via trajectory analysis and multidimensional observability metrics. Contribution/Results: Empirically validated in an Autonomous CloudOps scenario, our framework uncovers behavioral biases missed by conventional methods, robustly captures and represents agent uncertainty, and establishes a novel benchmark for trustworthy AI agent evaluation.

Technology Category

• Multiagent Systems: Multiagent Systems under Uncertainty
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Recent advances in agentic AI have shifted the focus from standalone Large Language Models (LLMs) to integrated systems that combine LLMs with tools, memory, and other agents to perform complex tasks. These multi-agent architectures enable coordinated reasoning, planning, and execution across diverse domains, allowing agents to collaboratively automate complex workflows. Despite these advances, evaluation and assessment of LLM agents and the multi-agent systems they constitute remain a fundamental challenge. Although various approaches have been proposed in the software engineering literature for evaluating conventional software components, existing methods for AI-based systems often overlook the non-deterministic nature of models. This non-determinism introduces behavioral uncertainty during execution, yet existing evaluations rely on binary task completion metrics that fail to capture it. Evaluating agentic systems therefore requires examining additional dimensions, including the agent ability to invoke tools, ingest and retrieve memory, collaborate with other agents, and interact effectively with its environment. We propose an end-to-end Agent Assessment Framework with four evaluation pillars encompassing LLMs, Memory, Tools, and Environment. We validate the framework on a representative Autonomous CloudOps use case, where experiments reveal behavioral deviations overlooked by conventional metrics, demonstrating its effectiveness in capturing runtime uncertainties.

Problem

• Evaluating non-deterministic behavior in agentic AI systems
• Assessing multi-agent collaboration and tool usage capabilities
• Capturing runtime uncertainties beyond binary task completion

Innovation

• Multi-agent architectures for coordinated reasoning and execution
• End-to-end framework with four evaluation pillars
• Captures runtime uncertainties beyond binary task completion