{"ID":2891759,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.16459","arxiv_id":"2507.16459","title":"Towards Enforcing Company Policy Adherence in Agentic Workflows","abstract":"Large Language Model (LLM) agents hold promise for a flexible and scalable alternative to traditional business process automation, but struggle to reliably follow complex company policies. In this study we introduce a deterministic, transparent, and modular framework for enforcing business policy adherence in agentic workflows. Our method operates in two phases: (1) an offline buildtime stage that compiles policy documents into verifiable guard code associated with tool use, and (2) a runtime integration where these guards ensure compliance before each agent action. We demonstrate our approach on the challenging $τ$-bench Airlines domain, showing encouraging preliminary results in policy enforcement, and further outline key challenges for real-world deployments.","short_abstract":"Large Language Model (LLM) agents hold promise for a flexible and scalable alternative to traditional business process automation, but struggle to reliably follow complex company policies. In this study we introduce a deterministic, transparent, and modular framework for enforcing business policy adherence in agentic w...","url_abs":"https://arxiv.org/abs/2507.16459","url_pdf":"https://arxiv.org/pdf/2507.16459v2","authors":"[\"Naama Zwerdling\",\"David Boaz\",\"Ella Rabinovich\",\"Guy Uziel\",\"David Amid\",\"Ateret Anaby-Tavor\"]","published":"2025-07-22T11:00:37Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}