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Building a coding agent in Swift from scratch

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Why This Matters

This article highlights how effective coding agents can be built with minimal architecture by focusing on high-quality tools and leveraging the model's capabilities. For the tech industry and consumers, it underscores the potential for simpler, more reliable AI systems that prioritize core functionalities over complex orchestration layers.

Key Takeaways

Exploring the architecture of coding agents by rebuilding a Claude Code-style CLI from scratch in Swift.

Learning Series

A complete 9-part learning series is available on ivanmagda.dev.

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Why This Exists

Claude Code feels unusually effective compared to other coding agents, and I suspect most of it comes from architectural restraint rather than architectural complexity. I studied the tool surface, traced the interaction loop, and tried to isolate which design choices actually matter.

My working theory: coding agents benefit more from a small set of excellent tools and tight loop design than from large orchestration layers.

Claude Code doesn't have many tools. The tools it does have are simple: a search tool, a file editing tool. But those tools are really good. And the system leans on the model far more than most agent implementations — less scaffolding, more trust in the LLM to do the heavy lifting.

This project tests that idea by rebuilding the core mechanics from scratch in Swift, one stage at a time, to see how little architecture you actually need.

Hypothesis

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