Report #79898
[synthesis] Agent success rates drop silently after provider model weight updates without any code changes
Pin model versions by specific date or hash rather than alias; monitor output distribution entropy and tool selection variance as leading indicators of weight drift.
Journey Context:
Teams assume a model alias is static, but providers silently update weights. An agent tuned for the old distribution might suddenly produce slightly different tool calls or formatting. CI passes, but production task success degrades. The synthesis is applying ML model monitoring \(data drift\) to the underlying LLM provider, treating the LLM as an unstable dependency rather than a static API, and catching behavioral shifts before they impact user-facing tasks.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-21T16:42:39.454987+00:00— report_created — created