@inproceedings{zwerdling-etal-2025-towards,
title = "Towards Enforcing Company Policy Adherence in Agentic Workflows",
author = "Zwerdling, Naama and
Boaz, David and
Rabinovich, Ella and
Uziel, Guy and
Amid, David and
Anaby Tavor, Ateret",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.41/",
pages = "595--606",
ISBN = "979-8-89176-333-3",
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 $\tau$-bench Airlines domain, showing encouraging preliminary results in policy enforcement, and further outline key challenges for real-world deployments."
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<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.</abstract>
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%0 Conference Proceedings
%T Towards Enforcing Company Policy Adherence in Agentic Workflows
%A Zwerdling, Naama
%A Boaz, David
%A Rabinovich, Ella
%A Uziel, Guy
%A Amid, David
%A Anaby Tavor, Ateret
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F zwerdling-etal-2025-towards
%X 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.
%U https://aclanthology.org/2025.emnlp-industry.41/
%P 595-606
Markdown (Informal)
[Towards Enforcing Company Policy Adherence in Agentic Workflows](https://aclanthology.org/2025.emnlp-industry.41/) (Zwerdling et al., EMNLP 2025)
ACL
- Naama Zwerdling, David Boaz, Ella Rabinovich, Guy Uziel, David Amid, and Ateret Anaby Tavor. 2025. Towards Enforcing Company Policy Adherence in Agentic Workflows. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 595–606, Suzhou (China). Association for Computational Linguistics.