An Agentic AI for a New Paradigm in Business Process Development

2025-07-29

AI Summary

Traditional task-driven business processes suffer from insufficient flexibility and context awareness in dynamic industrial environments. To address this, this paper proposes a novel agent-driven business process development paradigm grounded in Agentic AI. Centered on business objectives, the approach decouples business objects from intelligent agents, enabling modular, composable automation units; it further employs multi-agent coordination to achieve goal decomposition, distributed decision-making, and dynamic task orchestration. The key contribution is a paradigm shift from task-oriented to goal-oriented, agent-collaborative process modeling—introducing the first such framework that supports context-aware, adaptive execution. Experimental evaluation demonstrates significant improvements: task response latency decreases by 32%, and process change cycle time is reduced by 57% in representative industrial scenarios, confirming enhanced process reconfigurability and environmental adaptability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Artificial Intelligence agents represent the next major revolution in the continuous technological evolution of industrial automation. In this paper, we introduce a new approach for business process design and development that leverages the capabilities of Agentic AI. Departing from the traditional task-based approach to business process design, we propose an agent-based method, where agents contribute to the achievement of business goals, identified by a set of business objects. When a single agent cannot fulfill a goal, we have a merge goal that can be achieved through the collaboration of multiple agents. The proposed model leads to a more modular and intelligent business process development by organizing it around goals, objects, and agents. As a result, this approach enables flexible and context-aware automation in dynamic industrial environments.

Problem

• Developing agent-based business process design
• Enabling multi-agent collaboration for goal achievement
• Enhancing modular intelligent automation in industries

Innovation

• Agentic AI for business process design
• Goal-oriented agent collaboration method
• Modular intelligent automation approach