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Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback — arXiv:2608.15591

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.15591
License (as archived): CC0-1.0
Archived: 2026-10-18T17:25:56.576Z
SHA-256 of the archived text: 8698ed7ba930fb12f14d2124c820f17091172ec5866b5115f7692307c8d0b87b
Stable URL of this page: https://metraipolis.com/documento/dc_5b431d760b10

How to cite

Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback — arXiv:2608.15591 — arXiv (en), https://arxiv.org/abs/2608.15591 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5b431d760b10 (Metraipolis archive dc_5b431d760b10, sha256 8698ed7ba930fb12f14d2124c820f17091172ec5866b5115f7692307c8d0b87b)

BibTeX

@misc{dc5b431d760b10,
  title = {Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback — arXiv:2608.15591},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.15591},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5b431d760b10; sha256 8698ed7ba930fb12f14d2124c820f17091172ec5866b5115f7692307c8d0b87b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.15591. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deployment time, while the business rules and edge cases it must handle continue to evolve. Existing approaches address agent construction and one-time evaluation but provide no structured mechanism for continuous post-deployment behavioral correction without modifying the agent's source code. Most of the approaches offered in the market, require intense collection of logs and traces, and re-examining the agent design by the engineering team, a process which is heavy, long and negates the economical value of agentic transformation. We introduce Agent Gym, a modular, domain-agnostic framework that wraps any existing LLM-based agent in a continuous evaluation-and-evolution loop. The framework provides six composable capabilities --- Act, Evaluate, Investigate, Correct, Learn, and Observe --- organized across three architectural zones: a constitution layer that codifies domain knowledge in configuration artifacts, a runtime inference pipeline that chains acting, investigation, and adaptive correction, and a learning loop that enables subject matter experts to discover and validate new correction rules through natural language interaction. The key technical contributions 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 guarantees rule correctness before human approval. We further introduce the Spec-to-Note Gap, an autoencoder-inspired view of agentic system transparency. An open-source reference implementation for invoice processing demonstrates that the framework is fully operational and ready for adoption.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-18 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.15591 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T16:29:13.812Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).