The original title is about silent drift in agent decision quality and how to catch it. Let me rewrite this for a mobile feed.

Short summary

Silent quality drift in production agents surfaces as support tickets, not broken traces. Catch it by instrumenting decision attributes (decision type, tools, version), building golden baselines from hand-checked cases, sampling production traffic (random for unbiased reads, targeted for anomalies), and grading samples with a pinned LLM judge. Re-run baselines in CI on every prompt or model change; continuously sample and grade production runs to detect regressions before users do.

• •Quality drift manifests as support tickets, not error logs—you need monitoring beyond traditional observability dashboards
• •Establish golden baselines from hand-checked cases and track decision distributions (decision type, tools, version) across runs
• •Use random + targeted sampling with LLM-judge grading to catch regressions in CI and production

Generated with AI, which can make mistakes.