Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems

2026-08-04

AI Summary

Current AI systems struggle to meet system-level requirements—such as reliability, resilience, and accountability—in complex industrial and commercial settings, largely due to the absence of a unified decision-action integration framework. This work proposes a decision-centric intelligent architecture that introduces the concept of enaction into AI design for the first time. It establishes a quadruple synergistic mechanism comprising an organizational world, a situated world, pattern intelligence, and an enactive decision loop, thereby closing the perception-action cycle. By integrating organizational behavior modeling, situated optimization, multi-model coupling, and self-evolving decision-making, the framework unifies tools, agents, and existing AI applications. It enables governable, scalable, and socially valuable enterprise-grade AI deployment, offering a new paradigm for next-generation industrial AI systems.

Abstract

As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image generation tasks, increasingly integrating tools, agents, and harnesses to solve real business and industrial problems. However, the power of AI is not verified under these real-world complex systems for various reasons, considering reliability, feasibility, resilience, and responsibility requirements in real commercial and industrial operations. This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback. Four complementary roles organize the framework: an Organizational World defines operations management logic and an organizational behavior world model behind an enterprise from a strategic-institutional horizon; a Site World defines a physically bounded industrial optimization and execution world model from an operational-realization horizon; Schema Intelligence provides the coupling mechanism between two world models to weave various AI applications via two models; and Enactive Decision Cycle triggers the self-evolving dynamic process to update and audit the entire framework. By foregrounding decision intelligence in complex systems, Enactive AI expands the frontier of AI from model capability to system-aware action, opening new possibilities for scalable, governable, and socially valuable AI deployment. Enactive AI points toward a future in which AI progress is measured not only by what models can generate or automate, but by how reliably intelligent systems can support consequential action, responsible governance, and durable social value in the complex systems that shape modern life, which we believe will define the next frontier of AI research for enterprise-level and industrial complex systems.

Problem

• Enactive AI
• complex systems
• decision intelligence
• reliability
• responsible AI

Innovation

• Enactive AI
• Decision-Centric Architecture
• Complex Systems
• Schema Intelligence
• Self-Evolving Framework