Absolutely. In fact, there are cases where building internally makes a lot of sense. If your organization has a narrow problem with well-understood inputs, a stable expected output, and knowledge that is genuinely unique to your company, a custom agent may be the right solution.

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The calculation changes when that one capability starts expanding into a system. A production engineering AI application may need to interpret CAD, connect to PLM and other tools, preserve permissions, retrieve internal standards and historical decisions, coordinate several specialized capabilities, and present the results somewhere engineers will actually use them. That is much more than connecting an LLM to company data.

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So the build-or-buy question is not simply, “Can our engineers build an AI agent?” Many capable organizations can. A better question might be: “Which AI capabilities are unique enough that we should own them ourselves, and how much of the surrounding engineering AI system do we also want to own and maintain?”