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Chapter 7 · 2026

Log analysis is necessary for credible evaluation of AI agents

Peter Kirgis, Sayash Kapoor, Stephan Rabanser

Abstract

This paper argues that log analysis is crucial for credible evaluation of AI agents, as outcome-based benchmarks alone can be misleading. It presents a taxonomy of threats to credible evaluation, including inflated/deflated scores and concealed dangerous actions. The authors propose guiding principles for log analysis and illustrate them with a case study, offering recommendations for benchmark creators, model developers, and evaluators.

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 AI agents, evaluation, log analysis 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

AI agentsevaluationlog analysisbenchmarkingcredibility