为长篇写作项目构建一个由人工智能协调的出版工作流程
我一直在尝试一种以编排为先的方法，而不是依赖单一模型来处理整个工作流程。<p>这个项目最初是为了探索一个真实的客户场景，随着进展，它演变成了一个涉及多个专业代理、MCP/A2A服务、评估循环和支持基础设施的长篇出版工作流程。<p>到最后，系统处理了260亿个标记，发展到包括25个代理和工具、30个代理技能、27个打包技能、22个项目、12个MCP/A2A原生服务、8个全栈服务（API + UI + MCP + A2A）、318个拉取请求和423次提交。<p>作为首席技术官，我的目标是在要求我的工程团队以这种新方式构建之前，深入了解一个真实的客户场景。<p>项目链接在这里：https://theisaiahchronicles.com<p>我很想听听其他人是如何处理编排和多代理系统的。
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I've been experimenting with an orchestration-first approach instead of relying on a single model to handle an entire workflow.<p>The project started as a way to explore a real customer scenario, and along the way it evolved into a long-form publishing workflow involving multiple specialized agents, MCP/A2A services, evaluation loops, and supporting infrastructure.<p>By the end, the system had processed 26 billion tokens and grown to include 25 agents and tools, 30 agent skills, 27 packaged skills, 22 projects, 12 MCP/A2A-native services, 8 full-stack services (API + UI + MCP + A2A), 318 PRs, and 423 commits.<p>My goal as CTO was to go deep into a real customer scenario myself before asking my engineering organization to build in this new way.<p>The project is here: https://theisaiahchronicles.com<p>Would love to hear how others are approaching orchestration and multi-agent systems.