Definition
What Is Agent Failure Diagnosis?
Agent failure diagnosis is the systematic process of analyzing AI agent behavior to identify root causes of failures — not just symptoms — and prioritize fixes by impact.
Definition
Agent failure diagnosis is the process of tracing AI agent behavior across multi-step tasks to identify, classify, and rank failure modes by root cause. Unlike simple output scoring, diagnosis examines the full execution trace — planning decisions, tool calls, state management, and error recovery — to determine why an agent failed, not just that it failed.
Why Diagnosis Matters More Than Scoring
A benchmark score tells you an agent fails 30% of the time. Diagnosis tells you that 15% of failures are planning errors, 10% are tool misuse, and 5% are knowledge gaps. This breakdown transforms a vague quality problem into specific, prioritized engineering tasks.
Key Components of Failure Diagnosis
Effective diagnosis includes: trace analysis (following agent behavior step-by-step), failure taxonomy (categorizing errors by root cause), severity ranking (prioritizing by impact and frequency), and data-gap mapping (connecting failures to specific training data deficiencies).
From Diagnosis to Fix
Diagnosis drives a closed-loop improvement cycle: identify failure modes → generate expert training data targeting those gaps → retrain → verify with regression tests. Without diagnosis, data collection is unfocused and improvement is slow. BakeLens implements this diagnosis-to-fix pipeline for production agent teams.
FAQ
Frequently Asked Questions
What is agent failure diagnosis in AI?
Agent failure diagnosis is the systematic process of tracing AI agent behavior across multi-step tasks to identify, classify, and rank failure modes by root cause. It goes beyond output scoring to analyze planning, tool use, state management, and error recovery.
How is agent diagnosis different from evaluation?
Evaluation measures performance (pass/fail, accuracy). Diagnosis explains why failures happen — classifying root causes like planning errors, tool misuse, or knowledge gaps — and connects them to specific data or training fixes.
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