If you’ve used GitHub Copilot, Claude Code, Cursor, or OpenAI Codex and wondered what’s actually happening under the hood when the agent “thinks,” plans a multi-step fix, and edits three files at once — Simon Willison just published the definitive practitioner answer.

Willison’s new chapter of Agentic Engineering Patterns — titled “How Coding Agents Work” — is the clearest technical breakdown yet of what separates a coding agent from a coding assistant, and why that distinction matters enormously for how you use and build with these tools.

The Core Architecture: It’s Simpler (and More Surprising) Than You Think

Willison’s central insight is that coding agents aren’t magic. They’re LLMs plus a structured harness — but the harness is doing far more work than most practitioners realize.

Here’s the actual architecture:

1. A Very Long System Prompt

Coding agents like Claude Code and OpenAI Codex start with system prompts measured in hundreds of lines. Not dozens — hundreds. These prompts establish:

  • The agent’s identity and operating constraints
  • How it should approach different categories of tasks
  • Specific instructions for tool use, error handling, and when to ask for clarification
  • Safety rails and behaviors to avoid

Willison includes OpenAI Codex’s March 2026 system prompt as a reference example — and it’s illuminating. The level of detail is remarkable. The system prompt isn’t just “you’re a helpful coding assistant” — it’s a dense operational manual.

Practical takeaway: If you’re building your own coding agent, invest heavily in the system prompt. It’s not boilerplate — it’s the primary mechanism that shapes agent behavior.

2. A Tool Harness

The agent can’t do anything useful without tools. At minimum, a capable coding agent needs:

  • Read file — look at existing code
  • Write file — make changes
  • Execute code/terminal — run tests, commands, build steps
  • Search — find relevant files or documentation
  • Ask for clarification — pause and check with the human

The tool harness is what transforms an LLM from a text generator into an agent that can change things. The LLM outputs structured tool call requests; the harness executes them and feeds results back.

3. Multi-Step Planning Loops

What looks like “the agent fixing a bug” is actually an iterative loop:

  1. Read the relevant files
  2. Reason about what’s wrong and what to change
  3. Write the fix
  4. Execute the tests
  5. Observe results
  6. Repeat if tests fail or new information changes the plan

This loop can run 5, 10, or 20+ times for a complex task. Each iteration feeds the agent new information from the environment. The “intelligence” of a good coding agent is largely its ability to maintain coherent plans across these iterations without losing context or contradicting earlier decisions.

4. Context Window Management

Here’s a constraint Willison highlights that trips up many practitioners: the context window is finite and fills up.

As an agent executes, it accumulates:

  • The original task description
  • System prompt (hundreds of lines)
  • Tool call history and results
  • File contents it’s read
  • Error messages and test output

On a long-running task, this can push toward or past the model’s context limit. Good agent harnesses manage this explicitly — truncating older history, summarizing completed steps, or using selective memory to keep working context tight.

Practical takeaway: If your agent fails on long tasks, context overflow is often the culprit. Design your harness to manage context actively.

Reading Real System Prompts

One of the most valuable parts of Willison’s guide is pointing practitioners toward real published system prompts. Anthropic, OpenAI, and others have released or had leaked the system prompts for their coding agents — and reading them is genuinely educational.

What you’ll notice:

  • Extensive instructions for handling ambiguity (“if the user’s request is unclear, ask before writing code”)
  • Tool use precedence rules (“prefer existing functions over writing new ones”)
  • Safety constraints expressed as explicit rules, not vague guidelines
  • Persona and communication style guidance

Understanding these patterns helps you write better system prompts for your own agents and helps you work more effectively with existing coding agents by understanding what they’re actually optimized for.

Putting It Together: A Minimal Coding Agent

If you wanted to build a minimal coding agent today, Willison’s framework suggests you need:

  1. An LLM with tool calling (Claude, GPT-4o, Gemini — all work)
  2. 4-6 filesystem and shell tools (read, write, execute, search)
  3. A system prompt that establishes operating constraints (~100+ lines is normal)
  4. A loop that runs until the task is done or the agent gives up
  5. Context management so the loop doesn’t collapse under its own history

That’s it. The frontier coding agents are doing a lot more — better planning, smarter context management, richer tool sets — but the core pattern is achievable with a weekend of implementation.

Sources

  1. Simon Willison — How Coding Agents Work (Agentic Engineering Patterns)
  2. Hacker News Discussion
  3. Simon Willison on Mastodon — Guide Announcement

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260316-2000

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