Enactive AI: A Decision-Centric Architecture for Complex Systems
A recent paper on arXiv (2608.03413) presents Enactive AI, a conceptual framework aimed at enhancing reasoning in enterprises and industries, providing decision support at the site level, and facilitating execution feedback. This framework seeks to tackle the complexities of implementing AI in real-world systems, where factors like reliability, feasibility, resilience, and responsibility are paramount. Enactive AI comprises four interconnected roles: an Organizational World that outlines operations management logic and a model of organizational behavior from a strategic perspective; a Site World that delineates a specific industrial operation; and two additional roles that are not specified in the abstract. The study integrates related research and positions Enactive AI as a decision-focused architecture for complex systems, moving beyond traditional large language models to address real business challenges. It highlights that AI's effectiveness in these environments remains unverified, proposing Enactive AI as a viable solution. The paper can be found on arXiv under the 'new' announcement category.
Key facts
- Paper ID: arXiv:2608.03413
- Announcement type: new
- Introduces Enactive AI as a conceptual framework
- Framework for enterprise and industry reasoning, site-level decision support, and execution feedback
- Addresses reliability, feasibility, resilience, and responsibility in real commercial and industrial operations
- Four complementary roles: Organizational World, Site World, and two others
- Organizational World defines operations management logic and organizational behavior world model
- Site World defines a physically bounded industrial operation
- Synthesizes adjacent research
- Moves beyond LLMs and text/image generation to integrate tools, agents, and harnesses
Entities
Institutions
- arXiv