The new constraint is judgment.
AI makes it easier to produce code. That changes the pace of implementation, but it does not decide which problem deserves attention, how a system should fit together, or what trade-offs a business can accept. Those decisions remain central to good product development.
A feature always belongs to a wider system.
Consider a payment flow. The visible form is only one part of the work. There are account states, failures, refunds, permissions, support processes, and information moving between systems. Faster implementation helps, but an experienced team knows to ask about the parts that are not on the first screen.
Give agents context and boundaries.
A useful AI workflow starts with a clear task and enough context to do it well. It also needs a boundary: what the agent can change, what it should verify, and when a person must make the decision. That structure allows a team to use automation without losing responsibility for the result.
Use the speed to learn.
The opportunity is not simply to produce more output. It is to get useful software in front of the right people earlier, discover what works, and adjust while the cost of change is still manageable. A smaller, well-understood release can teach a team more than a large set of untested assumptions.
Our position.
We are all-in on AI and agentic development. We also believe the strongest outcomes come from experienced people directing that capability. Architecture, context, review, and responsibility are part of the work. AI gives those skills more reach.