Self-Optimizing Factories: The Role of Agentic Automation in Industry 4.0

Abstract

Autonomous AI agents have revolutionized artificial intelligence by developing from specific task automation to independent goal-oriented systems which reason and act autonomously. This research investigates the fundamental design elements of agentic automation through the perception-cognition-action-memory cycle which allows agents to function in changing environments including self-driving cars and AI-based healthcare diagnostics. The paper examines contemporary frameworks LangGraph, CrewAI and AutoGen to demonstrate their contribution to developing multi-agent collaboration and distributed intelligence systems. The paper evaluates technical innovation alongside ethical and governance challenges which include accountability gaps and bias in autonomous decision-making and legal phenomenology of AI agency. The paper uses case studies from autonomous vehicles and enterprise AI integration to demonstrate both the potential and risks of agentic systems. We conclude by identifying open research questions about safety protocols and human-AI trust frameworks and propose future work on self-improving agents and neuro-symbolic hybrid architectures. The research combines theoretical knowledge with practical applications to establish a complete guide for researchers and practitioners who work with autonomous AI agents.