As AI writes more code, observability becomes the key to catching agent business-logic failures
mipsytipsy · x · 2026-10-22
A shared thread argues that as AI writes more code, fewer people understand how it behaves in production, making observability more important — and changing what to expect from it.
Example: a support agent processes a refund; every service succeeds, latency is normal, the model's answer is coherent, the refund API works as designed — but the agent refunds the wrong order. Business-logic failures aren't new; what changes with agents is how much logic gets decided at runtime via prompts, retrieved data, memory, and earlier action results.
Infra teams are building agents to investigate incidents, and AI teams are building tools to trace and evaluate agents — these efforts are converging: agents need production data to work, and we need the same data to verify their work.
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