Claude Code Architecture: Why a Simple Loop Beat Complex Graphs

Problem

Coding agents were toys for a long time. Complex DAG schemas where developers hardcoded every transition: “if user wants X — go here, if Y — go there”. Systems became rigid and brittle.

Claude Code works differently. Jared Zoneraich from PromptLayer broke down the architecture at a recent AI Engineer conference. Turns out, under the hood it’s not a decision graph — it’s a simple loop.

Context

Why did complex schemas fail? Developers tried to program agent behavior imperatively. Every edge case required a new condition. Every condition — a potential failure point.

Modern models are smart enough to handle uncertainty. They can explore, make mistakes, and self-correct. Rigid schemas get in the way.

Claude Code philosophy: give the model tools and step aside.

Solution

Loop Instead of Graph

Claude Code architecture is a while loop:

while has_tool_calls(response):
    results = execute_tools(response.tool_calls)
    response = model.generate(results)

While there are tool calls — execute them, pass results to the model, repeat. No predetermined paths.

Bash as Universal Adapter

One of the key tools is plain Bash. Why it works:

Massive training data exists for Bash
Model can write a Python script, run it via Bash, get results, and delete the file
Instead of creating hundreds of specific tools — one universal adapter

The agent tries approaches and fixes errors on the fly. Bash gives it that flexibility.

Context is the Enemy

The more context in the prompt, the worse the model performs. Claude Code solves this several ways:

Sub-agents. For specific tasks (reading docs, running tests) separate agents with clean context are created. They return only the result, not polluting the main thread.

Diff instead of rewrite. The agent doesn’t rewrite files entirely — it creates diffs. Saves tokens and reduces error probability.

To-do lists. The model maintains a structured action plan. This helps stay on track and resume after failures.

Skills for Customization

Skills are extensible system prompts for specific tasks. Updating documentation in your style, deep repository research, team-specific processes.

This allows adapting the agent without overloading the main context.

Insight

Three principles that explain how modern coding agents work:

Trust the model. Don’t try to program every case. Models handle exploration and self-correction well.

Simple architecture. Zen of Python (“simple is better than complex”) works for agents too. Loop instead of graph.

Context management. Efficient memory handling matters more than tool count. Sub-agents, diffs, to-do lists — all about context.

We’re moving from copy-pasting code from chat to headless agents that create PRs and fix bugs on their own. Not because models got smarter — but because we learned to deliver context properly.