Production Learning Review — Page 2

Shadow deployment validates agent changes against live traffic before users see them.

Five testing layers catch failures that single-layer evaluation misses entirely.

Tiered checks and statistical gates replace expensive single-judge sweeps on every PR.

Benchmark scores don't measure whether agents reach production safely.

Offline evals can pass while production agents fail in ways frozen test sets never see.

Real production traces catch multi-turn regressions that static benchmarks cannot see.

Multi-layer monitoring catches agent drift that system-wide signals miss.

Detecting loops requires watching trajectory patterns, not individual steps.

Securing agents requires monitoring every data entry point, not just user input.

Infrastructure failures, not model defects, drive most production agent breakdowns.

Schema drift silently breaks agent reliability without triggering existing safeguards.