POLARIS: Typed Planning and Governed Execution for Agentic AI in Back-Office Automation
It addresses the need for predictable and compliant automation in enterprise workflows, which is an incremental improvement over generic multi-agent setups.
The paper tackles the problem of creating auditable and policy-aligned agentic AI systems for enterprise back-office automation by introducing POLARIS, a framework that uses typed planning and governed execution, achieving a micro F1 of 0.81 on the SROIE dataset and 0.95 to 1.00 precision for anomaly routing in synthetic tests.
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