AI-assisted coding changes the starter kit you should begin with
Coding agents are productive in proportion to the context they are given. A typed, well-bounded monorepo with written instructions is the real AI feature.
When you pair with a coding agent, the bottleneck is rarely how fast it can type. It is how much of your project it can hold in its head correctly. The same is true of a new human teammate. That makes the shape of your starter — not a bolted-on AI panel — the thing that determines how useful AI assistance will be.
Context beats cleverness
An agent that can see your package boundaries, your types, and your project
rules will make fewer wrong guesses than one staring at an undifferentiated
blob of code. This starter ships with written instructions (an AGENTS.md,
Convex guidelines, and per-package context) that describe the rules which
override generic assumptions. That is
agent-ready architecture, not a hidden model you have
to trust.
Types are guardrails
Strong types narrow what an agent can write incorrectly. When functions and validators are typed end to end, a wrong field name or a missing argument is a red squiggle, not a runtime surprise discovered in production. Review gets faster because the compiler has already done the boring half.
Boundaries make changes safe
UI, config, analytics, email, and backend live in separate packages with clear contracts. An agent asked to change billing does not need to understand your button component, and vice versa. Smaller, well-labelled surfaces are easier for both people and models to modify without collateral damage.
What it does not mean
It does not mean an agent can safely build an entire product unattended. It means the cost of each assisted change drops, because the context is already there. We are deliberate about that distinction — you can read more in the FAQ.
The takeaway: if AI assistance is part of how you build now, start from a codebase that gives the assistant a map. A typed, bounded, documented foundation is the feature.