Use Case
Agent Reliability
Agents fail where it matters: planning, tools, ambiguity.
The Problem
Where agents break down
Standard benchmarks test isolated capabilities. Real deployments expose compounding failures across long task chains.
Planning failures in 10+ step task chains that benchmarks never test
Tool calls that return correct formats but wrong results, silently
Ambiguous user instructions that expose hardcoded fallback behavior
How It Works
From failure to fix in four stages
BakeLens maps the failure surface. Proof delivers the training data to close the gap. Automated regression ensures fixes stick.
Detection
Analysis
Fix
Deliver
Detection
Analysis
Fix
Deliver
BakeLens maps the failure surface
Trace planning, tool calls, and recovery across full task runs
Rank failures by frequency × severity
Compare across agent versions, prompts, and model swaps
Proof delivers targeted training data
Expert-labeled multi-turn interactions for diagnosed failures
Verified tool-use sequences with correct intermediate states
Adversarial edge cases targeting your agent's weak points
What You Get
Deliverables
Failure Mode Report
Prioritized list of failure modes with traces, frequency, and severity scores
Targeted Training Data
Expert-labeled datasets built against diagnosed gaps, not generic benchmarks
Reliability Eval Suite
Evaluation set that catches the failures you fixed, so they don't come back
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