Components of A Coding Agent

Components of A Coding Agent How coding agents use tools, memory, and repo context to make LLMs work better in practice In this article, I want to cover the overall design of coding agents and agent harnesses: what they are, how they work, and how the different pieces fit together in practice. Readers of my Build a Large Language Model (From Scratch) and Build a Large Reasoning Model (From Scratch) books often ask about agents, so I thought it would be useful to write a reference I can point to. More generally, agents have become an important topic because much of the recent progress in practical LLM systems is not just about better models, but about how we use them. In many real-world applications, the surrounding system, such as tool use, context management, and memory, plays as much of a role as the model itself. This also helps explain why systems like Claude Code or Codex can feel significantly more capable than the same models used in a plain chat interface. In this article, I lay out six of the main building blocks of a coding agent. Claude Code, Codex CLI, and Other Coding Agents You are probably familiar with Claude Code or the Codex CLI, but just to set the stage, they are essentially agentic coding tools that wrap an LLM in an application layer, a so-called agentic harness, to be more convenient and better-performing for coding tasks. Coding agents are engineered for software work where the notable parts are not only the model choice but the surrounding system, including repo context, tool design, prompt-cache stability, memory, and long-session continuity. That distinction matters because when we talk about the coding capabilities of LLMs, people often collapse the model, the reasoning behavior, and the agent product into one thing. But before getting into the coding agent specifics, let me briefly provide a bit more context on the difference between the broader concepts, the LLMs, reasoning models, and agents. On The Relationship Between LLMs, Reasoning Models, and Agents An LLM is the core next-token model. A reasoning model is still an LLM, but usually one that was trained and/or prompted to spend more inference-time compute on intermediate reasoning, verification, or search over candidate answers. An agent is a layer on top, which can be understood as a control loop around the model. Typically, given a goal, the agent layer (or harness) decides what to inspect next, which tools to call, how to update its state, and when to stop, etc. Roughly, we can think about the relationship as this: the LLM is the engine, a reasoning model is a beefed-up engine (more powerful, but more expensive to use), and an agent harness helps us the model. The analogy is not perfect, because we can also use conventional and reasoning LLMs as standalone models (in a chat UI or Python session), but I hope it conveys the main point. In other words, the agent is the system that repeatedly calls the model…