The system
A team of small, focused agents — each with one job, each working against an explicit contract.
Most AI code generation relies on a single large model with full codebase context for every task. The Clean React agent workflow does the opposite — it breaks every feature into its smallest constituent parts and assigns each part to a dedicated local agent with only the context it needs. Nothing more.
The role of local agents
Small models handling precise tasks at near-zero cost.
Local agents are the engine of the workflow — handling the bulk of every feature build entirely on your own machine, with no cloud API costs and no unnecessary context.
What they handle
Every domain module, every connector, every atomic UI component — built by a dedicated local agent working against its spec and TypeScript contract. 80–90% of the entire codebase, handled locally.
Why local models are the right tool
Large cloud models are powerful but expensive — and most of that power is wasted on tasks that don't need it. A local agent given a tight spec and an explicit contract doesn't need repo-wide understanding. It needs to read a contract and implement against it correctly.
How they operate
Each agent receives two things — its implementation spec and its TypeScript contract. That is its entire context. It writes its part, produces its output, and hands off.
Why this produces consistent output
Every agent works against the same architectural standard. Every part it produces follows the same structure and the same contracts as every other part. Consistency is guaranteed by the system before a single line is written.
The role of Claude Code
Repo-wide intelligence,
used only where it's needed.
Claude Code is the most capable — and most expensive — tool in the workflow. The agent workflow is designed to use it precisely where it earns its cost, and nowhere else.
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What it handles
Claude Code is responsible for two things — creating the full UI and feature specs before the agents begin, and wiring every feature together once the agents have finished.
Both tasks require repo-wide context and high-level reasoning. Both are exactly what Claude Code is built for.
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Used at two stages, nowhere else
Every prompt sent to Claude Code costs tokens based on context size. In a standard AI development workflow, almost every prompt carries full codebase context — whether it needs it or not.
In the Clean React agent workflow, Claude Code only sees the full codebase at the two points where that context is genuinely necessary. Every other task goes to a local agent with a minimal context.
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The result
Claude Code spend is reduced to a fraction of what a standard workflow would cost — because it is used precisely, not habitually.
The quality of its output is also higher, because it is given tasks that match its capabilities rather than tasks that any smaller model could handle.
Get in touch
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