HomeResearch LibraryA Survey on Evaluation of LLM-based Agents
Chapter 7 · 2025

A Survey on Evaluation of LLM-based Agents

Yipeng Li, Mahmoud Mohammadi, Jane Lo

Abstract

This survey provides a comprehensive overview of the evaluation methodologies for LLM-based agents. It categorizes existing approaches, discusses common challenges, and highlights key metrics used to assess agent performance, reliability, and safety. The paper aims to bring clarity to the fragmented landscape of LLM agent evaluation and identify future research directions.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper directly addresses one of the core structural challenges in Zone III deployments. The research on LLM-based agents, evaluation, survey provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

LLM-based agentsevaluationsurveybenchmarkingmetrics