The more I think about AI-native SDLC, the more I wonder whether the traditional software house model needs a complete transformation.
A client arrives with a specific project, a problem, and an expected outcome. The software house designs an SDLC process for that project: selecting tools and agents, providing context, and defining operating rules, tests, safeguards, and the points where human decisions are needed.
The entire process, from gathering requirements through specification and implementation to testing, deployment, and monitoring, is then largely carried out by AI agents. The software house’s role is to oversee it, verify quality, and continuously improve it.
The output is still software. What changes is how it is built.
The software house no longer primarily sells developers’ time. It becomes an SDLC factory, configuring the right process for each project to turn an idea into a working product.
This shift also requires openness from engineers. Their work will increasingly involve designing, overseeing, and improving the system that produces the solution, with less time spent manually building every part of it.