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Chapter 3 · 2026

AgentRx: Diagnosing AI Agent Failures from Execution Trajectories

Shraddha Barke, Arnav Goyal, Alind Khare

Abstract

AI agents often fail in ways that are difficult to localize due to probabilistic, long-horizon, multi-agent executions and noisy tool outputs. This paper addresses this by manually annotating failed agent runs and releasing a novel benchmark of 115 failed trajectories. It also presents AGENTRX, an automated diagnostic framework that pinpoints critical failure steps and categories, improving step localization and failure attribution over existing baselines.

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 contributes useful building blocks for Zone III architecture through its work on AI agent failures, diagnosis, execution trajectories. While not exclusively focused on enterprise deployment, the insights translate directly to the challenges of long-horizon agentic workflows. The key lesson for Zone III practitioners: the problems identified here do not disappear at scale — they compound. Understanding them at the research level is prerequisite to solving them in production.

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 agent failuresdiagnosisexecution trajectoriesmulti-agent systemserror localizationfailure attribution