Not all developers need to understand the deep internals of AI or how AI enhanced development tools work under the hood. In practice, however, existing tools do not always work optimally for a given project or situation. That gap is exactly why we need someone who can create AI based tooling, such as agent pipelines, that serve as a practical tool for product owners, designers, software developers, test engineers, and others.
The core knowledge required for this role is twofold. First, the AI tool engineer needs a solid understanding of how AI models actually work, what they need in order to perform well, where they fall short, and how to work around those limitations. Second, they need to be able to implement the tooling itself, of course with AI assistance, including context management, orchestration, and all the supporting structures that make these systems usable and reliable.
I see this role as similar to the internal machine shops that large factories used to have. A specialized crew maintained and customized production machinery to keep everything running smoothly. They built custom tools when needed to improve efficiency and quality. The factory did not expect every worker on the floor to rebuild a machine from scratch, but someone had to know how.
Who should consider transitioning into this role? Software developers with a solid understanding of AI internals and some hands-on experience with agentic processes would be a natural fit. In this role, they could deepen their AI expertise and push beyond the limits of off-the-shelf tooling, which is usually designed to work reasonably well for everyone and optimally for no one.
Vendors like OpenAI, Anthropic, and Google will continue to produce generic tools. These tools will cover most common use cases but will never be perfectly tuned for your specific context. An AI tool engineer can take that last step, refining those tools or building on top of them to give your teams a real advantage in performance and quality.
