aidebuggingobservability

AI mistake debugger

Build a tool that identifies and classifies recurring errors in AI planner outputs.

Why now

Bigger models still make consistent mistakes; teams need better debugging than raw logs.

Who for
AI production teams
Business model
Per-agent monitoring fees
Effort
A few weeks

AI planners fail in predictable ways, but finding patterns requires manual log diving.

A debugger that clusters similar failures, suggests prompt adjustments, and tracks improvement over versions.

Charge per monitored agent. Critical for production AI systems.

Start with simple error classification, then add automatic mitigation suggestions.

Risk: May need deep integration with specific planner architectures.

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AI mistake debugger — Ideas