The Engineers Who Can’t Use AI Agents Don’t Have a Tools Problem
Your engineers struggling with AI agents don’t need better tools or training. They need permission to work differently and the organizational barriers removed that punish speed.
The efficacy of new engineering capabilities depends upon the organization's existing intellectual capital and its ability to externalize that knowledge.
Invest in explicit knowledge, or new tools will fail.
Organizational knowledge, often assumed and implicit, becomes a critical constraint when integrating novel tooling such as agent-driven development. These tools demand explicit, structured input.
The ability to articulate context, system behavior, and design rationale is a core competency, distinct from pattern-matching or operational proficiency within an existing system.
Practices like pair programming, architecture review, and structured learning foster the externalization of knowledge and the development of deep system understanding.
Investing in explicit knowledge directly addresses the underlying capability gaps that new technologies illuminate, rather than merely addressing tool-specific challenges.
The first question for any AI program: what does this organization measure, and what does the measurement reward?
The efficacy of new engineering capabilities depends upon the organization's existing intellectual capital and its ability to externalize that knowledge.
Invest in explicit knowledge, or new tools will fail.
Organizational knowledge, often assumed and implicit, becomes a critical constraint when integrating novel tooling such as agent-driven development. These tools demand explicit, structured input.
The ability to articulate context, system behavior, and design rationale is a core competency, distinct from pattern-matching or operational proficiency within an existing system.
Practices like pair programming, architecture review, and structured learning foster the externalization of knowledge and the development of deep system understanding.
Investing in explicit knowledge directly addresses the underlying capability gaps that new technologies illuminate, rather than merely addressing tool-specific challenges.
The first question for any AI program: what does this organization measure, and what does the measurement reward?
After 20 years in software development, Norman is both a hands-on leader and defining the new age of AI SDLC for some of the biggest brands in the world — and exploring it with the builders. He writes here about things he is hearing and seeing. All posts are his personal points of view and do not reflect any employer or any customer he has ever had contact with.
The views and opinions expressed in this article are the author’s own and do not represent the positions of any employer, client, or affiliated organization.
