Self-Evolving APIs: A Framework for AI-Driven Middleware that Autonomously Refactors, Optimizes, and Documents Endpoints from Real Time

Abstract

APIs serve as critical contracts connecting modern distributed systems, directly impacting reliability, performance, cost, and developer productivity. Current API evolution remains largely manual: engineers detect usage changes via logs, plan breaking changes, refactor code and clients, and update documentation. This process is slow, error-prone, and poorly suited to continuously changing environments. This study proposes self-evolving API middleware that leverages telemetry to detect usage shifts, automatically propose and apply safe refactoring, reconfigure runtime behavior, and maintain synchronized documentation. The framework presented here combines continuous telemetry ingestion, LLM-driven refactoring recommendations, reinforcement learning for runtime policy tuning, and an explainability layer for auditable automated changes. The approach reduces mean time to remediation by 86%, lowers operational costs by 28%, and minimizes compatibility incidents while preserving developer intent and safety.