AI Coding Agents Changed Small Teams. Review Matters.
AI coding agents can write, test, and deploy. For small teams, the real competitive advantage is disciplined review and clear ownership.
Two years ago, "AI in coding" meant a better autocomplete. Today it means an agent you hand a task to, let it read the codebase, propose a plan, and write the diff - while you just review it.
At Legacies, we're two people. We don't have a QA team, we don't have a DevOps department. Here's what actually changed for us:
Reviewing became the job, not writing
We spend less time writing boilerplate and more time reading diffs and asking "why did the agent choose this approach." It's a shift in role, not just speed - you have to be good at code review even if you're not the fastest typist.
Small, well-described tasks win
An agent given "fix bug X, here's the reproduction, here's the file" delivers consistently. An agent given "make the site better" gets you into trouble. We learned to write tasks the way you'd write a ticket for a junior colleague, not a vague wish.
Generated code has to be treated as yours, not "the AI's"
If it ships to production, it's your responsibility. It doesn't matter who wrote the first version of the function - if you don't understand why it works, it isn't ready to deploy.
What hasn't changed
Architecture, product decisions, and visual taste remain human work. A good agent executes a good direction fast; it doesn't give you the direction.
For a small studio, the real difference isn't "we write code faster" - it's that we can take on projects we would have turned down three years ago for lack of time.