Breakdown of Multi-Model Agentic Workflow in Claude Code

Recently, a practical guide to building a multi-model agentic workflow within Claude Code has attracted attention in the community. This setup breaks the limitations of single-model execution, aiming to maximize the strengths of different LLMs through refined division of labor, thereby saving token consumption and improving overall development efficiency.

Key Configurations and Role Division

The core of this workflow lies in clear role allocation: using Fable 5 (set to maximum reasoning strength) as the global orchestrator for planning and task decomposition. Two sub-agents with fixed models are created via the /agents command: deep-reasoner uses Opus for reasoning-intensive tasks like architecture design and complex debugging, while fast-worker uses Sonnet for mechanical execution like template code and testing. Additionally, OpenAI Codex is introduced as a peer engineer (rather than a reviewer) to process high-risk tasks in parallel with Opus, providing different perspectives.

Implementation Steps and Details

In terms of specific operations, the author shared a complete configuration process. First, install the local Codex CLI, and add the official OpenAI Codex plugin via the Claude Code plugin market command. Then, write the carefully designed orchestration instructions into the project's CLAUDE.md file to ensure the orchestrator can accurately dispatch various sub-agents and external tools. This workflow structure was eventually organized by community members into 9 visualized charts for developers to easily understand and replicate.

2026-07-07 ~ 2026-07-09 · 5 related posts