AI 智能体开始自己给自己造工具
现在的 AI 智能体大多像一次性工具：每个任务配一个专门系统，换个领域就得重造。这篇提出一个反过来的思路——让 AI 自己搭建、改进、组合自己的「工具链」，把每个模型和它的专属配置当成一个可插拔的零件，由总调度按任务拆解、分派、拼装。在复杂长任务上，它明显超过现有最强系统。它不是你明天就能用上的东西，但它指向一个趋势：AI 的进化不再只是模型变强，而是系统学会自己给自己造工具。
📄 原文摘要(英文)
As large language models advance, AI agents are moving beyond isolated, domain-specific tasks toward long-horizon, cross-domain workflows. This transition exposes two challenges: increasing harness complexity makes manual design difficult to scale, while tighter coupling to specific domains limits the generality of a single harness. The central question thus shifts from how to engineer a stronger harness for one domain to how to autonomously construct specialized harnesses, improve them through experience, and orchestrate them across domains. We introduce Raven, The Harness of Harnesses, an open-source multi-agent ecosystem that automatically constructs and evolves modular harnesses for specific models and domains, treating each executable model--harness pair as a composable unit of intelligence. To support an All-Domain Collaboration Network, its Host Agent decomposes goals, matches subtasks to specialized agents, coordinates execution dependencies, and integrates results, while a host archive and EverOS preserve experience across tasks and Skill Forge makes that experience available as reusable procedures. Our theory establishes sufficient conditions for such composition to expand reliable task coverage beyond that of the available individual agents under a shared resource budget. On complex and long-horizon tasks, Raven significantly outperforms the state-of-the-art agent systems, pushing the frontier of composable agentic intelligence.