POLARIS：面向后台自动化代理AI的类型化规划与受控执行
POLARIS: Typed Planning and Governed Execution for Agentic AI in Back-Office Automation
AI总结：
POLARIS通过类型化规划和受控执行框架，提升后台自动化中代理AI的政策一致性与可预测性，实现高精度任务处理与审计追踪。
摘要：
企业后台工作流需要具备可审计性、政策一致性以及操作可预测性的代理系统，而通用多代理设置往往难以满足这些要求。我们提出了POLARIS（Policy-Aware LLM Agentic Reasoning for Integrated Systems），一个受控编排框架，将自动化视为基于LLM代理的类型化计划合成和验证执行。规划器提出结构多样、类型检查的有向无环图（DAGs），一个由标准引导的推理模块选择一个合规的计划，执行则由验证器门控检查、有界修复循环和编译政策护栏保障，这些护栏在问题发生前阻止或路由副作用。应用于以文档为中心的金融任务，POLARIS能够生成决策级的成果并提供完整的执行轨迹，同时减少人工干预。实证结果表明，POLARIS在SROIE数据集上实现了微F1值为0.81，在受控合成数据集上实现了异常路由的精度为0.95至1.00，同时保持审计轨迹。这些评估构成了受控代理AI的初始基准。POLARIS为政策一致的代理AI提供了方法论和基准参考。关键词：代理AI、企业自动化、后台任务、基准、治理、类型化规划、评估
英文摘要：
Enterprise back office workflows require agentic systems that are auditable, policy-aligned, and operationally predictable, capabilities that generic multi-agent setups often fail to deliver. We present POLARIS (Policy-Aware LLM Agentic Reasoning for Integrated Systems), a governed orchestration framework that treats automation as typed plan synthesis and validated execution over LLM agents. A planner proposes structurally diverse, type checked directed acyclic graphs (DAGs), a rubric guided reasoning module selects a single compliant plan, and execution is guarded by validator gated checks, a bounded repair loop, and compiled policy guardrails that block or route side effects before they occur. Applied to document centric finance tasks, POLARIS produces decision grade artifacts and full execution traces while reducing human intervention. Empirically, POLARIS achieves a micro F1 of 0.81 on the SROIE dataset and, on a controlled synthetic suite, achieves 0.95 to 1.00 precision for anomaly routing with preserved audit trails. These evaluations constitute an initial benchmark for governed Agentic AI. POLARIS provides a methodological and benchmark reference for policy-aligned Agentic AI. Keywords Agentic AI, Enterprise Automation, Back-Office Tasks, Benchmarks, Governance, Typed Planning, Evaluation
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