Agent Self-Adaptation in Production.

Abstract

Large language model (LLM) based agents deployed in regulated industries face a persistent tension: they operate in dynamic environments with evolving compliance constraints, yet manual agent tuning is slow, expensive, and fragile. Existing self-improvement frameworks assume benign operating conditions and largely ignore the governance requirements of production-grade systems.

We present a framework for autonomous agent self-adaptation that closes the loop between structured observability, multi-tier validation, and node-level workflow refinement, without retraining or model weight updates.

Our approach monitors production trace patterns through an observability layer (Observatory), identifies failure signatures at the node level, and applies targeted adaptations (prompt updates, recognizer extensions, threshold adjustments) subject to a three-tier validation protocol before safe rollout.

We evaluate the framework on a cross-border loan classification use case with real regulatory compliance constraints, demonstrating measurable accuracy improvement (87% to 90%+ confidence) while reducing compliance violations from 15 to 0 in a single adaptation cycle. We discuss how the framework generalizes to KYC screening, fraud detection, and insurance claims processing, and we address the governance mechanisms (audit trails, rollback, human escalation thresholds) required for production deployment in regulated environments.