agent-nirzabari-0-1-0

name	agent-nirzabari-0-1-0
description	Comprehensive guide to understanding and building coding agent harnesses, covering 7-layer architecture, context engineering, tool orchestration, safety systems, and real-world implementations from Codex, OpenCode, Cursor, and Claude Code. Use when designing agentic systems, analyzing agent architectures, or studying production-grade coding agent implementations.

Coding Agent Harness Architecture

Overview

An LLM is neither a coding agent nor a product. A coding agent product is a text generation model coupled with a harness — a runtime that repeatedly builds context, calls the model, executes tool calls, persists state, renders to the user, recovers from failures, and loops back. This guide covers the 7-layer architecture of production-grade coding agents, drawn from analysis of real codebases including Codex (OpenAI), OpenCode, Claude Code, and Cursor.

The gap between a simple loop like while :; do cat PROMPT.md | claude-code; done and what Codex or Claude Code actually ship is product engineering. That gap — context management, safety boundaries, persistence, recovery, and multi-client support — is the subject of this skill.

When to Use

• Designing agentic systems that go beyond simple prompt loops
• Analyzing agent architectures across different products (Codex, OpenCode, Claude Code, Cursor)
• Implementing harness patterns for context building, tool orchestration, or safety
• Studying production-grade coding agent implementations and their trade-offs
• Understanding the LLM provider contract (streaming events, tool calling, multi-turn state)
• Building scalable autonomous coding systems with parallelization and specialization

Core Concepts: The 7 Layers

A coding agent harness consists of 7 layers:

1. Agent Loop — Conversation turns: system prompt → user → model → tools → model → ... Each turn can include hundreds of tool calls. Context management is the agent's core responsibility.