PolicyGuide: From Guarding One Action to Guiding the Whole Workflow for Policy-Compliant LLM Agents
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Abstract (arXiv)
Customer-service LLM agents must follow organizational policy when acting on a user's behalf. Compliance failures arise from either forbidden actions, such as granting an ineligible change, or omitted procedural requirements, such as identification or confirmation. Runtime safeguards can intervene on risky actions, but action-local checks do not guide an agent through a multi-step procedure. Workflow-following systems support prescribed process execution, but primarily target workflow completion rather than safeguarding agent behavior. PolicyGuide instead compiles each domain policy into a workflow graph and invokes a proactive verifier at user-turn boundaries. From persisted graph state, the verifier reconciles open requests and returns step-specific remediation along a policy-compliant path. Across the τ2-bench airline, retail, and telecom domains with a GPT-5.4 agent and verifier, PolicyGuide raises mean Pass4 from 0.42 to 0.62, with the largest gain on telecom (0.19 to 0.61), the most workflow-structured domain. The same workflows transfer to Claude Sonnet 4.6 and Gemini 2.5 Pro agents. Complementary evaluations find the lowest observed attack-success rate under adversarial users and the strongest procedural compliance in an author-designed workflow-level validation.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 21 of its 47 references that are in the map. First posted on arXiv 20 Aug 2026.
Builds on
21 of 47 references.
References · 21
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
- 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
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- 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agentscited by 223
- 2025AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectoriescited by 108
- 2025AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detectioncited by 89
- 2024StateFlow: Enhancing LLM Task-Solving through State-Driven Workflowscited by 65
- 2025CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactionscited by 47
- 2023ProAgent: From Robotic Process Automation to Agentic Process Automationcited by 26
- 2025IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systemscited by 25
- 2025FlowAgent: Achieving Compliance and Flexibility for Workflow Agentscited by 19
9 more
- 2025Towards Enforcing Company Policy Adherence in Agentic Workflowscited by 18
- 2026Solver-Aided Verification of Policy Compliance in Tool-Augmented LLM Agentscited by 16
- 2024FLAP: Flow-Adhering Planning with Constrained Decoding in LLMscited by 11
- 2025SOP-Agent: Empower General Purpose AI Agent with Domain-Specific SOPscited by 11
- 2026Beyond IVR: Benchmarking Customer Support LLM Agents for Business-Adherencecited by 11
- 2025Effective Red-Teaming of Policy-Adherent Agentscited by 10
- 2026PolicyGuard: A Dialogue-Grounded Sub-Agent Verifier for Policy Adherence in LLM Agentscited by 6
- 2023State Machine of Thoughts: Leveraging Past Reasoning Trajectories for Enhancing Problem Solvingcited by 4
- 2026Near-Miss: Latent Policy Failure Detection in Agentic Workflowscited by 4
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Citing papers per year: 2026: 3.
3 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Policy-Invisible Violations in LLM-Based Agents
- 2026AgentFlow: A Flow-Centric Policy Language and Framework for Securing LLM Agent Systems
- 2026Ghost in the Context: Policy-Carriage Integrity in LLM Agents
- 2026ActGov: Governing LLM Agent Actions via Policy-Constrained Validation
- 2026Guardrails as Infrastructure: Policy-First Control for Tool-Orchestrated Workflows
- 2026The Verifier Tax: Horizon Dependent Safety Success Tradeoffs in Tool Using LLM Agents
- 2026Governance by Construction for Generalist Agents
- 2026PolicyBank: Evolving Policy Understanding for LLM Agents