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empirical studyChapter 1arXiv · 2024

Towards Reliable AI Agents: A Framework for Systematic Failure Analysis

Research Team (Carnegie Mellon University)

Abstract

We present a systematic framework for analyzing failures in AI agent systems, covering failure mode identification, root cause analysis, and mitigation strategy development. The framework is validated on 500+ real agent failures.

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 is the most empirically grounded failure analysis in the corpus. The 500+ real failure analysis provides the ground truth for what actually goes wrong in production agent deployments — far more valuable than theoretical failure taxonomies.

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.

Key Contributions

  • →Systematic failure analysis framework
  • →500+ real failure analysis
  • →Mitigation strategy taxonomy

Topics

failure analysisreliabilityagent failuresroot cause analysis