As enterprise AI systems scale to handle complex workflows, practitioners face the challenge of routing subtasks to the right tools and skills. Agents can have hundreds of tools and skills and get confused on which one to use for each step of a workflow. To address this challenge, researchers at Alibaba developed SkillWeaver, a framework that creates an execution graph for a given task and chooses the right skills for each of the nodes. They also introduce Skill-Aware Decomposition (SAD), a novel technique that uses a feedback loop to enable the agent to fetch and vet relevant tool candidates iteratively. This compositional approach and feedback loop mechanism distinguishes SkillWeaver from other tool-routing frameworks that choose tools in a one-shot fashion.  SkillWeaver relates to real-world AI applications where agents autonomously orchestrate multi-tool ecosystems, such as the Model Context Protocol (MCP), to execute multi-step business operations like downloading datasets, transforming information, and creating visual reports.  In practice, the researchers’ experiments with SkillWeaver show that implementing this retrieve-and-route approach significantly increases accuracy while reducing token consumption by over …