Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback
| Source: arXiv AI
Tags: LLM agents, agentic AI, continuous learning, human-in-the-loop, enterprise AI, agent frameworks
Agent Gym is an open-source framework that wraps any deployed LLM agent in a continuous evaluation-and-evolution loop, letting subject matter experts discover and validate behavioral corrections via natural language — without touching the agent's source code or requiring engineering teams to re-examine logs and traces from scratch.
Details
Production LLM agents face a fundamental mismatch: behavior is frozen at deployment, but business rules and edge cases keep evolving. Current solutions require engineers to collect logs, re-examine agent design, and redeploy — a slow, expensive cycle that erodes the economic case for agentic automation.\n\nAgent Gym introduces a modular wrapper with six composable capabilities: Act, Evaluate, Investigate, Correct, Learn, and Observe. The architecture has three zones: a constitution layer encoding domain knowledge in configuration artifacts; a runtime pipeline chaining acting, investigation, and adaptive correction; and a learning loop where subject matter experts discover new correction rules through natural language interaction.\n\nKey technical components include a hybrid deterministic-LLM correction engine with 21 condition operators and three-tier actions, a three-layer investigation architecture for ground-truth-free compliance validation, and a programmatic safety loop that verifies rule correctness before human approval. An open-source reference implementation for invoice processing demonstrates the framework is functional. The paper also introduces the 'Spec-to-Note Gap' — an autoencoder-inspired framing of the transparency gap between what an agent is supposed to do and what it actually does. This is an early-stage but practically grounded contribution for enterprise AI teams.