An introduction to AI-driven development
AI agents can write code — but they're only as good as the context they're given. Here's how to set them up to actually help.
AI coding agents have gone from novelty to daily tool in a remarkably short time. But there's a wide gap between teams that get real leverage from them and teams that mostly fight with them. The difference is rarely the model. It's the context.
Agents don't lack intelligence — they lack context
A capable agent dropped into your codebase with no project context behaves like a brilliant contractor on their first day: fast, confident, and frequently wrong about what you actually want.
It doesn't know which milestone this work belongs to, what decisions were already made, or why a previous approach was abandoned. So it guesses — and guesses compound.
Specs are the interface
The most reliable way we've found to give agents context is versioned specifications: short, structured documents that describe what a feature should do, why, and what "done" looks like.
A good spec does three things:
- Anchors the work to a milestone and a goal, not just an isolated task.
- Records decisions so the agent doesn't re-litigate settled questions.
- Defines acceptance criteria the agent can check its own work against.
When the spec is the source of truth, the agent stops guessing and starts executing.
A simple loop that works
In practice, AI-driven development tends to settle into a rhythm:
- Define the work as a spec with clear acceptance criteria.
- Hand it to the agent along with the surrounding project context.
- Review the result against the criteria — not line by line, but outcome by outcome.
- Capture any new decisions back into the spec for next time.
Each pass makes the next one cheaper, because the context keeps getting richer.
Keep humans on the outcomes
AI-driven development doesn't remove judgment from the process — it relocates it. Instead of writing every line, you spend your attention deciding what should be built and whether what came back is right.
That's the shift worth making. Give agents the context they need, hold them to clear criteria, and keep humans focused on outcomes. The tools will keep getting better. The teams that win are the ones that get the context right.