Claude Code: Building a 20k Line Agent via Prompting

A developer built a 20,000-line AI agent by breaking the project into small, testable modules and using a manifest.json as a source of truth to prevent code collapse, proving that prompt engineering is the new high-level programming language.

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/