engineering-engineering-autonomous-optimization-architect

Use shadow testing: route a small percentage of live traffic asynchronously to the experimental model without affecting production responses. Grade outputs with a predefined mathematical rubric covering accuracy, latency, and formatting before promoting any model.

A circuit breaker monitors an endpoint's failure velocity and cost, then automatically stops routing traffic to it when thresholds are exceeded. It fails over to a cheaper designated fallback provider and alerts a human administrator.

Set a hard max cost per execution and a retry cap on every external call, and trip the circuit breaker on anomalies like a 500% traffic spike or repeated HTTP 402/429 errors. Never allow open-ended retry loops or unbounded API calls.

Yes, by ranking providers on a historical optimization score combining speed, cost, and accuracy, then updating router weights when an experimental model statistically outperforms the baseline. All promotion decisions must come from shadow-test evidence, not vendor benchmarks.

Autonomous promotion only works with statistically meaningful shadow-test sample sizes and explicit grading criteria established upfront. Subjective evaluation or testing on non-representative data produces unreliable routing decisions, and every fallback path must be validated in advance.