AI agents have made convincing demos cheap.
In our product work at XPER, the first convincing version is rarely the hard part.
The product work starts after it:
- the integrations,
- the edge cases,
- the quality issues,
- the operational headaches,
- the code the team now has to maintain.
AI can speed up delivery. It can also create technical debt faster when a team confuses “generated quickly” with “engineered properly”.
The real test is whether the first version survives real use. That is where the next thousands of commits live.
The teams I have seen do this well use agents for speed and stay strict about architecture and ownership. They put that discipline into repository instructions, tests, and mandatory review steps.
An agent should participate in the whole engineering process:
- understand the issue,
- inspect the existing system,
- propose a plan,
- implement the smallest useful change,
- run verification,
- report evidence,
- leave a reviewable diff.
Without that process, faster generation leaves a larger pile of code for somebody else to understand. With it, agents can help the team deliver faster without creating an unmanaged review queue.