[–] prateekdalal 4mo ago ↗ 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 qualityObservabilityTracingBut 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 entitiesGenerate deterministic fingerprints for new executionsMatch against prior failuresGate execution before the mistake repeatsThis 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.
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[ 2.3 ms ] story [ 10.3 ms ] threadFailures 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.