How It Works
How BakeLens Diagnoses Agent Failures
BakeLens provides trace-level agent diagnosis: following every planning decision, tool call, and recovery attempt to classify failures by root cause and connect them to fixes.
Trace-Level Behavior Analysis
BakeLens ingests full agent execution traces — every planning step, tool call, intermediate result, and recovery attempt. Instead of scoring only the final output, it reconstructs the agent's decision chain to identify exactly where and why failures occur.
Failure Taxonomy and Classification
Each failure is classified into a structured taxonomy: planning errors (wrong decomposition, lost state), tool-use errors (incorrect calls, misinterpreted results), knowledge gaps (missing domain facts), and reasoning errors (wrong inference steps). This classification transforms symptoms into actionable root causes.
Severity Ranking by Frequency and Impact
Not all failures are equal. BakeLens ranks failure modes by frequency × severity, producing a prioritized list that tells engineering teams exactly where to focus. A rare catastrophic failure may outrank a common minor one.
Data-Gap Mapping
The most valuable diagnosis output is the data-gap map: each failure mode is connected to specific training data deficiencies. 'Planning failures in multi-tool tasks' maps to 'need expert-labeled multi-tool interaction sequences.' This bridges diagnosis and data engineering.
Regression Testing
After fixes are applied, BakeLens generates regression evaluation sets from the original failure cases. These ensure that fixed failure modes stay fixed across model updates, prompt changes, and agent architecture changes.
FAQ
Frequently Asked Questions
How does BakeLens diagnose agent failures?
BakeLens ingests full agent execution traces, reconstructs the decision chain, classifies each failure by root cause (planning, tool use, knowledge, reasoning), ranks failures by frequency × severity, and maps them to specific training data gaps.
What is a failure taxonomy for AI agents?
A failure taxonomy is a structured classification system for agent errors: planning errors, tool-use errors, knowledge gaps, and reasoning errors. It transforms vague quality complaints into specific, prioritized engineering tasks.
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