An AI prototype can look convincing after a few successful examples. Production is different: inputs vary, dependencies fail, costs fluctuate, and users find edge cases the original team never imagined.

Define Success Before Shipping

Create a representative evaluation set and score the behaviors that matter. Accuracy may be important, but so are latency, cost, consistency, safety, and the percentage of cases that require human review.

Make Every Decision Observable

Capture model versions, prompts, tool calls, response times, and failure reasons without exposing sensitive data. Good traces turn vague reports into issues engineers can reproduce and fix.

Build Explicit Fallbacks

Set timeouts, retry limits, and deterministic alternatives. When confidence is low or a dependency is unavailable, the system should degrade gracefully instead of inventing an answer or blocking an entire workflow.

Improve With Real Evidence

Production feedback should become new evaluation cases. This creates a repeatable loop: observe failures, reproduce them, improve the system, and verify that the change did not damage behavior that already worked.