这篇论文提出了一个很实用的思路：在现有工作流引擎上加一个智能体层，不用重写系统就能让工作流更灵活。用贷款审批的例子讲清楚了三种智能体怎么协作，技术方案具体可落地。
论文提出进程架机制，在不替换底层工作流引擎的前提下，用策略治理的智能体层包裹确定性工作流。作者开发了任务-决策-流（TDF）模型，定义数据模式和执行语义，将LLM推理分解为三类策略治理的智能体：TaskAgent（知识密集型任务）、DecisionAgent（逐案例网关路由）和FlowAgent（运行时流适应）。在CUGA FLO中实现该设计，并通过贷款审批工作流演示三种智能体类型及挂钩驱动的监管覆盖。进程架通过确定性工作流执行强制结构合规，同时通过策略框架的智能体自主性满足规范需求。
A Process Harness for Uplifting Legacy Workflows to Agentic BPM: Design and Realization in CUGA FLO
We introduce the process harness, a new mechanism for uplifting legacy workflows into Agentic Business Process Management (Agentic BPM) without replacing the underlying workflow engine. A process harness places a policy-governed agentic layer around a deterministic workflow engine, intercepting designated control points to contribute reasoning, adaptation, and oversight while the engine retains structural authority over the process. To define the process harness rigorously, we develop the Task-Decision-Flow (TDF) model, specifying both its data schema and its execution semantics. TDF decomposes LLM reasoning across three policy-governed agent types: a TaskAgent for knowledge-intensive task execution, a DecisionAgent for per-case gateway routing, and a FlowAgent that governs runtime flow adaptation through a principled hook mechanism. Each agent reasons within an explicit policy drawn from the process FRAME, the aggregate policy set governing all LLM calls in the system. We then present CUGA FLO as the design and implementation realization of the TDF model, and demonstrate it on a loan approval workflow that exercises all three agent types and hook-driven regulatory override. The process harness uniquely reconciles imperative requirements, realized through deterministic workflow execution that enforces structural compliance, with normative requirements, realized through policy-framed agentic autonomy invoked at designated control points wherever the process demands it.