Journal of Product Design, Quality Engineering & Technology ISSN: 3139-2008 (Online)

Abstract

Engineering design workflows are becoming increasingly complex due to growing system requirements, multidisciplinary integration, and shortened development cycles. Traditional computer-aided design and simulation tools, while powerful, remain largely passive and require continuous human supervision. Recently, agentic artificial intelligence (AI) has emerged as a promising paradigm where autonomous or semi-autonomous AI agents actively participate in design decision-making, optimization, validation, and iteration. Unlike conventional AI models that respond to isolated prompts, agentic AI systems exhibit goal-oriented behavior, memory, tool usage, and self-reflection capabilities across the entire design lifecycle. This paper presents a comprehensive review of agentic AI applied to end-to-end engineering design workflows, covering conceptual design, modeling, simulation, optimization, verification, and lifecycle management. Key architectures, coordination mechanisms, human–AI collaboration models, and industrial case studies are discussed. The paper also highlights challenges such as trust, validation, scalability, and ethical considerations. Finally, future research directions are outlined, emphasizing the role of agentic AI in transforming engineering design from tool-driven to intent-driven workflows.

Keywords: Agentic AI, Engineering Design Automation, Multi-Agent Systems, End-to-End Design, Autonomous Engineering, AI-assisted Design