AI agents now write a large share of the code in my projects. They deliver fast and well, with a constant tendency to 

 rather than create a new concept. Request after request, a 20-line function grows to 100, a 100-line file grows to 500, and names only describe part of what they contain.

 corrects this drift with remarkable efficiency. Before each commit, my agent runs Fallow to measure the project's functions and files. Until the verdict is green, it keeps splitting. This procedure guarantees code made of small functions, organized in single-responsibility files, whose names say exactly what they do.

codeTidy

Fallow - Codebase intelligence

, released under the MIT license as a single Rust binary. It builds a complete graph of the repository (modules, exports, dependencies, functions) and derives four families of analyses from it:

: the same input always produces the same result. It runs on its own, without the TypeScript compiler or a Node.js runtime. According to the benchmarks published by the project, it can analyze a repository as large as the Next.js monorepo (

This determinism makes it the right partner for an AI agent: the agent proposes, Fallow measures.

sourceNote

At the pace agents work, I no longer review every line one by one. Asking an agent to “write simple code” does not ensure that the constraint is met.

Fallow becomes the safeguard of every commit. The diagram below shows the loop the agent follows for each task:

agentLoop

Development

Measurement

Splitting

Commit

Fallow does not say how to fix things: it points out what is too long, too nested, too central or repeated, and leaves it to the agent to 

 bot that automatically books my fitness classes, whose spots fill up within minutes. Once it was stable, I handed the project to an AI agent to integrate it into 

, with a graphical interface, controllable bookings and phone notifications.

The agent quickly delivered a working project, but every change had been grafted onto the existing code. A single function handled the API login, fetching the schedule, picking the time slot and then booking, and the whole interface lived in a single file.

Once Fallow was plugged in, the agent isolated each 

 in a dedicated function, with a name that says what it does. It then applied 

The result shows across three parts of the project:

A source of truth to keep the agent on track

An AI agent works on a project the way a developer would. It looks for the right file, reads what surrounds it and changes what it understands. A project generated prompt after prompt, never refactored, picks up the traits of 

 code from the very first requests: files with mixed responsibilities, approximate names, duplicated logic. In that context, finding the right file forces the agent to read more, and changing what it understands is no longer enough, because side effects multiply.

Fallow reverses this slope without requiring software architecture expertise. Splitting, naming and organizing rules are measured and the verdict is binding on the agent, without having to spell them out in every prompt or check them during review. The benefit shows on three levels:

benefitCards

Short functions and single-responsibility files, where every change stays confined to the part concerned.

Fewer regressions

Navigation guided by explicit names, without rereading entire files to find the right line.

Fewer tokens

An objective, reproducible criterion to validate the agent's work, where a natural-language instruction remains unverifiable.

More control

Hand Fallow to your agent. On every commit, it splits, names and organizes the code without you having to dictate each rule. You keep a readable project that costs less to evolve, and every new request starts from a healthy base. Quality is no longer decreed, it is verified.

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On one side, a locked-down, measurable architecture. On the other, an agent that executes tirelessly.

Together, they split, name and organize every line of code.

The duo that makes the ultimate software architect

Fallow measures, AI builds

Hands-on feedback: Fallow, a TypeScript static analyzer written in Rust, acts as the source of truth for my AI agents and guides them to split functions and files into clear, well-named, well-organized units.

Fallow and AI agents: a hands-on take on code quality

Hands-on feedback on a deterministic check for TypeScript

Fallow measures the complexity, dead code and duplication of a TypeScript project. Plugged into my AI agents, it turns every commit into a chance to split functions and files into well-named, well-organized units.

Fallow: code quality at the pace of AI agents

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