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:
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Memory management layer.
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Tool execution engine.
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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.
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