Plan-Augmented Multi-Agent LLM Systems for Enterprise Workflow Automation: A Thinking Fast and Slow Decision Framework (Journal of Advanced Artificial Intelligence Research, 2026)
The integration of large language models into enterprise workflows has introduced unprecedented capabilities in natural language understanding, generation, and reasoning. However, the deployment of single-agent LLMs for complex, multi-step, and coordination-intensive business processes reveals significant limitations in reliability, consistency, and adherence to organizational constraints. This paper proposes a plan-augmented multi-agent LLM architecture that leverages a dual-process decision framework inspired by Kahneman’s thinking fast and slow model. In this system, a set of specialized LLM agents operate under the supervision of a planning module that distinguishes between rapid reactive decisions and deliberative analytical reasoning. The architecture supports enterprise workflow automation by dynamically assigning tasks to fast or slow reasoning pathways based on task complexity, risk level, and temporal constraints. We discuss structural trade-offs, governance mechanisms, robustness considerations, and fairness implications. Through cross-domain comparisons and illustrative case studies, we demonstrate how the proposed framework enhances operational efficiency while maintaining accountability. The paper further examines deployment challenges, sustainability metrics, and policy implications for large-scale socio-technical infrastructures. Our analysis suggests that plan-augmented multi-agent systems offer a promising path toward reliable and scalable enterprise automation, provided that careful attention is given to interpretability, bias mitigation, and human oversight.
Keywords: multi-agent systems, large language models, workflow automation, thinking fast and slow, decision framework, enterprise architecture, governance