Constrained Process Maps for Multi-Agent Generative AI Workflows
This addresses the problem of managing uncertainty and coordination in multi-agent AI workflows for compliance and due diligence, representing an incremental advance with domain-specific applications.
The paper tackles the challenge of observing and comparing uncertainty and coordination in multi-agent generative AI workflows for regulated settings by introducing a formalized multi-agent system as a finite-horizon MDP. Results show improvements over a single-agent baseline, including up to a 19% increase in accuracy and an 85x reduction in required human review.
Large language model (LLM)-based agents are increasingly used to perform complex, multi-step workflows in regulated settings such as compliance and due diligence. However, many agentic architectures rely primarily on prompt engineering of a single agent, making it difficult to observe or compare how models handle uncertainty and coordination across interconnected decision stages and with human oversight. We introduce a multi-agent system formalized as a finite-horizon Markov Decision Process (MDP) with a directed acyclic structure. Each agent corresponds to a specific role or decision stage (e.g., content, business, or legal review in a compliance workflow), with predefined transitions representing task escalation or completion. Epistemic uncertainty is quantified at the agent level using Monte Carlo estimation, while system-level uncertainty is captured by the MDP's termination in either an automated labeled state or a human-review state. We illustrate the approach through a case study in AI safety evaluation for self-harm detection, implemented as a multi-agent compliance system. Results demonstrate improvements over a single-agent baseline, including up to a 19\% increase in accuracy, up to an 85x reduction in required human review, and, in some configurations, reduced processing time.