Over the past year, I’ve noticed something interesting in production AI systems:

Failures don’t just happen — they repeat.

Slightly different prompts. Different agents. Same structural breakdown.

Most tooling today focuses on:

Prompt quality

Observability

Tracing

But very few systems treat failures as structured knowledge that should influence future execution.

What if instead of just logging AI failures, we:

Store them as canonical failure entities

Generate deterministic fingerprints for new executions

Match against prior failures

Gate execution before the mistake repeats

This changes the boundary between “AI suggestion” and “system authority.”

Curious how others are thinking about structured failure memory in AI systems — especially once agents start touching real tools.