The CoLab Software article takes a grounded look at AI agents currently used in engineering design and offers practical guidance on what they can realistically do today, where they make the most impact, and how engineering leaders should evaluate them before adoption. It focuses squarely on manufactured-product design workflows—not software engineering, process engineering, or civil engineering—and emphasizes that these tools are already in use rather than hypothetical future tech.
AI agents for engineering design are defined not by buzzwords but by their ability to run a multi-step workflow autonomously, work inside a production environment (using structured data such as CAD models, drawings, and standards), and produce repeatable, testable, and predictable outputs without repeated prompting. They are workflow-specific and include clear triggers, responsibilities, and handoff points back to human engineers.
The article identifies tasks where agentic AI is already delivering value. One of the most mature applications is CAD and drawing review. These agents can scan models and 2D drawings, flagging design risks such as ambiguous notes, manufacturing-for-assembly issues, title-block or revision inconsistencies, and departures from internal standards. Because they apply the same rules every time, they reduce variability and human error in high-volume reviews.
Other examples include simulation-setup agents that assist engineers in preparing models and choosing simulation parameters, and lessons-learned agents that capture design issues and feedback from previous projects for use in future work. These agents help preserve corporate knowledge that might otherwise be scattered or lost.
The article stresses that no single agent handles an entire design process. Successful deployments start with low-risk, high-impact tasks that benefit from consistency and large information volumes. Leaders evaluating solutions should look beyond generic claims and ask whether the agent’s scope aligns with specific workflows, whether it actually accesses the right engineering data, and whether its outputs are predictable and supported by real engineering contexts.