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

Enterprise AI Agents: From Prototypes to Production

This workshop aims to bring together researchers and practitioners to examine how enterprise AI agents can successfully move from prototypes to production, and focuses on three pillars: 1) Agent architectures and systems; 2) Enterprise applications and deployments; 3) Evaluation and governance.

Artificial Intelligence agents and autonomous decision-making in business: a review

Agentic AI: Architectures, Types, Capabilities, Mathematical Equations and Governance in the Era of Autonomous Intelligence

Agentic Artificial Intelligence (Agentic AI) represents a major advancement in the evolution of intelligent systems by enabling autonomous planning, decision-making, and action execution. Unlike traditional AI models, which are primarily reactive and designed to respond to predefined inputs, Agentic AI systems possess capabilities such as memory, reasoning, goal-oriented planning, tool integration, and dynamic adaptation to changing environments. These characteristics allow them to perform complex, multi-step tasks with minimal human intervention, making them suitable for applications across healthcare, finance, cybersecurity, robotics, education, and enterprise automation. This paper explores the conceptual foundations, architectural principles, and practical applications of Agentic AI while examining the key differences between conventional AI and autonomous agent-based systems. It presents a taxonomy that distinguishes reactive and agentic models, discusses single-agent and multi-agent orchestration frameworks, and analyzes the role of memory, planning, perception, and external tool usage in intelligent decision-making. The study also highlights the ethical, security, governance, and accountability challenges associated with deploying autonomous AI, including issues related to transparency, privacy, bias, reliability, and human oversight. As AI systems become increasingly capable of operating independently within complex environments, establishing robust governance and regulatory frameworks is essential. This paper proposes a comprehensive framework for the design, evaluation, and responsible deployment of Agentic AI, emphasizing safety, explainability, human-in-the-loop supervision, and ethical compliance. By integrating technical innovation with effective governance strategies, the proposed approach aims to maximize the benefits of Agentic AI while minimizing potential risks. The findings contribute to the growing body of research on trustworthy autonomous systems and provide practical guidance for developing secure, reliable, and human-centered Agentic AI solutions.

The Multi-Agent Enterprise: From Silos to Autonomy

With the advent of Artificial Intelligence (AI), the world of enterprise automation has radically changed to an AI multi-agent ecosystem with coordination across functional teams and the capacity to make autonomous decisions. Despite this, many companies are still discontinuing the implementation of AI, with partial integration into their processes, weak systems integration, and a lack of a sense of network in some business units. It introduces the concept of the traditional enterprise transforming into an intelligent, autonomous enterprise with the help of AI in logistics, knowledge management, finances, HR, cybersecurity, compliance, customer support, and operational analytics, and also introduces the Multi-Agent Enterprise Framework (MAEF) as the scalable architecture. The proposed architecture has four layers: shared memory, human in the loop, policy-driven control, and orchestration layer, which are necessary for safe, transparent, and trustworthy cooperation between the set of specialized agents. Training is conducted in a highly realistic business environment that includes several departments, numerous workflow requests, and is evaluated and tested against standard automated and single-agent AI systems. Experimental results show that workflow automation and task completion time have been enhanced, cross-department collaboration has been effective, operational efficiency has been achieved, and resources are used optimally; meanwhile, the governance and compliance requirements are met. Agreeing with these conclusions, it seems that enterprise-wide multi-agent systems are a good building block for digital enterprises capable of adapting, scaling, and operating autonomously, on which future intelligent businesses would be able to operate.

Agentic AI Architectures for Autonomous Business Applications

Agentic Artificial Intelligence (Agentic AI) represents the next generation of intelligent systems capable of autonomous sensing, reasoning, planning, and action with minimal human intervention. Unlike traditional AI, Agentic AI integrates large language models, reinforcement learning, multi-agent systems, planning mechanisms, orchestration layers, and memory modules to enable adaptive and goal-oriented decision-making. This paper explores Agentic AI architectures for autonomous business applications, highlighting their role in finance, healthcare, supply chain, enterprise resource planning, customer relationship management, and industrial operations. A layered architecture comprising perception, reasoning, orchestration, and execution layers is proposed to support autonomous analysis, strategic planning, and optimized action execution. The framework also incorporates memory, monitoring, and governance modules to enhance transparency, reliability, and explainability. Experimental evaluation demonstrates that the proposed architecture improves workflow automation, decision accuracy, operational efficiency, resource utilization, and response time while reducing manual intervention. The findings indicate that Agentic AI provides a scalable and robust foundation for future autonomous enterprise systems. The study also discusses key challenges, including explainability, governance, ethics, and trust, emphasizing their importance for successful enterprise adoption. Overall, Agentic AI architectures offer significant potential to accelerate intelligent automation and drive the next generation of business transformation.

Grounded Multi-Agent Systems for Decision Support in Industrial Operations

Industrial operations need AI systems that can reason across live process data, engineering knowledge, and operator workflows. Yet conventional machine learning models often remain narrow predictors, while large language models lack grounding in plant behaviour, constraints, and real-time operating context. This talk presents Orbital, a grounded multi-agent system for decision support in industrial operations. Orbital combines three complementary layers: a time-series model for multivariable process dynamics and uncertainty-aware forecasting; a constraint-learning layer that extracts engineering relationships from plant documentation, including P&IDs, datasheets, mass and energy balances, and operating manuals; and a language-fusion layer that aligns process behaviour with engineering descriptions. These components are coordinated through specialist agents for planning, tool execution, verification, memory, and response composition. The system moves beyond prediction toward interpretable decision support: detecting abnormal behaviour, retrieving relevant historical events, explaining likely root causes, and grounding recommendations in both data and engineering constraints. More broadly, this work argues that the next generation of industrial AI must be grounded, multi-modal, and operationally trustworthy; connecting data, domain knowledge, and human decision-making in high-consequence environments.