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.

Key Contributions

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

Eigenvector Commentary

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.

Topics

failure analysisreliabilityagent failuresroot cause analysis

Relevance Scores

Long-Horizon Score92

Enterprise Score91

Completeness85

Paper Info

Year2024

VenuearXiv

Typeempirical study

ChapterCh. 1

Authors1

Frameworks

AEGIS PASF