{"id":19412,"date":"2026-10-27T09:12:04","date_gmt":"2026-10-27T13:12:04","guid":{"rendered":"https:\/\/blogs.mathworks.com\/deep-learning\/?p=19412"},"modified":"2026-10-27T09:12:04","modified_gmt":"2026-10-27T13:12:04","slug":"teaching-ai-agents-how-engineers-work","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/deep-learning\/2026\/10\/27\/teaching-ai-agents-how-engineers-work\/","title":{"rendered":"Teaching AI Agents How Engineers Work"},"content":{"rendered":"\r\n
\r\n\r\n\r\n
\r\n

Guest blogger: Akash Gopisetty<\/strong><\/p>\r\n

I'm pleased to welcome guest blogger Akash Gopisetty, Product Marketing Manager, Radar and Signal Processing<\/strong>. Akash explores how reusable skills help AI agents follow established engineering methods.<\/p>\r\n<\/td><\/tr><\/tbody>\r\n<\/table>\r\n\r\n

Connecting AI agents to engineering tools gives them the ability to write code, execute workflows and evaluate results, all within an established engineering environment. This ensures the results are inspectable, testable and verifiable. <\/p>\r\n

\"Grounding<\/p>\r\n

Figure 1: Grounding AI in engineering tools<\/p>\r\n

However, access to engineering tools alone does not provide the agent with the domain expertise needed to solve specialized engineering problems. That is where skills come in.<\/p>\r\n

Skills are a way to capture how an experienced engineer would approach a problem. They are especially useful in technical domains like wireless communication, signal processing, and RF and antenna design, where there may be multiple valid approaches and the right choice depends on domain-specific assumptions, selecting the right methods and engineering tradeoffs. For example, an RF or antenna engineer may need to translate requirements for frequency, gain, coverage and polarization into various antenna and array architecture. They may also need to factor in impedance matching, mutual coupling and array effects and evaluate whether the resulting design meets the system-level performance requirements. Skills could be used to capture this reasoning and guide the agent through a workflow, allowing the engineer to focus on reviewing the design choices and exercising judgement over the final design. <\/p>\r\n

Instead of asking an agent to discover or guess the right procedure at each prompt, engineers can codify proven methods into skills, version them like any other artifact and reuse them across relevant workflows and across compatible AI agents. <\/p>\r\n

Skills are especially valuable when workflows require:<\/p>\r\n