Papers by Guy Uziel
Towards Enforcing Company Policy Adherence in Agentic Workflows (2025.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) agents are transforming business processes with minimal human oversight. |
| Approach: | They propose a deterministic, transparent, and modular framework for enforcing business policy adherence in agentic workflows. |
| Outcome: | The proposed framework shows encouraging preliminary results in policy enforcement on the -bench Airlines domain. |
A Survey on Evaluation of LLM-based Agents (2026.findings-acl)
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Asaf Yehudai, Lilach Eden, Alan Li, Guy Uziel, Yilun Zhao, Roy Bar-Haim, Arman Cohan, Michal Shmueli-Scheuer
| Challenge: | This paper provides the first comprehensive survey of evaluation methods for LLM-based agents . LLMs are static, having fixed knowledge, and confined to text-to-text interaction. |
| Approach: | They analyze the evaluation of LLM-based agents across five perspectives . they identify current trends and key gaps in evaluation methods . |
| Outcome: | The proposed evaluation frameworks and tools are based on five perspectives . the results highlight current trends and identify gaps in future research . |
Text Augmentation Using Dataset Reconstruction for Low-Resource Classification (2023.findings-acl)
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| Challenge: | Existing methods for text classification use labeled data, but labeles are expensive and difficult to obtain. |
| Approach: | They propose a novel method of data augmentation using the text-generation capabilities of language models. |
| Outcome: | The proposed method improves the current state-of-the-art methods for data augmentation on multi-class datasets. |
Breaking ReAct Agents: Foot-in-the-Door Attack Will Get You In (2025.findings-naacl)
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| Challenge: | Indirect prompt injection attacks, prompted by harmless and unrelated requests, can significantly increase the likelihood of the agent performing subsequent malicious actions. |
| Approach: | They propose to implement a simple reflection mechanism that prompts the agent to reassess the safety of its actions during execution, which can help mitigate this vulnerability. |
| Outcome: | The proposed method reduces the success of such attacks by prompting the agent to reassess its actions during execution. |
Effective Red-Teaming of Policy-Adherent Agents (2025.emnlp-main)
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| Challenge: | Large Language Model (LLM)-based agents are increasingly used in domains with strict policies, such as refund eligibility or cancellation rules. |
| Approach: | They propose a multi-agent red-teaming system that leverages policy-aware persuasive strategies to undermine a policy-adherence agent in a customer-service scenario. |
| Outcome: | The proposed model outperforms jailbreak methods and tau-break to assess agent's robustness against manipulative user behavior. |