# Claude Code: Building a 20k Line Agent via Prompting

> Source: <https://promptcube3.com/en/threads/3473/>
> Published: 2026-07-26 01:46:27+00:00

# Claude Code: Building a 20k Line Agent via Prompting

The goal was to create a complex agent capable of multi-step reasoning and tool use, but the scale quickly became a nightmare for standard chat interfaces. Context windows get cluttered, and the AI starts hallucinating previous function definitions. To solve this, I shifted my AI workflow toward a more iterative, modular approach.

Instead of asking for the whole project at once, I treated the LLM like a senior architect and myself as the product manager. I broke the agent down into small, testable modules:

1. Memory management layer.

2. Tool execution engine.

3. The core reasoning loop.

The real struggle was the "regression loop"—where fixing a bug in module A would mysteriously break module B because the AI forgot a dependency. I diagnosed this by forcing the agent to generate a comprehensive `manifest.json`

of all current functions and their inputs/outputs before every new feature implementation. This acted as a source of truth that prevented the code from collapsing under its own weight.

For anyone trying a similar deep dive into LLM agents, the secret isn't in the "perfect prompt," but in the verification loop. If you don't have a way to programmatically test each 500-line chunk as it's generated, you'll spend more time debugging AI hallucinations than you would have spent just typing the code.

The result is a massive, functioning codebase that proves prompt engineering is essentially the new high-level programming language.

[Next Lattice Boltzmann Method: Simulating Fluids Without Navier-Stokes →](/en/threads/3466/)