Automating Agentic Workflow Generation

a novel framework for automating the generation of agentic workflows; it reformulates workflow optimization as a search problem over code-represented workflows, where edges connect LLM-invoking nodes; it efficiently explores the search space using a variant of MCTS, iteratively refining workflows through code modification, tree-structured experience, and execution feedback; experiments across six benchmark datasets demonstrate AFlow’s effectiveness, showing a 5.7% improvement over manually designed methods and a 19.5% improvement over existing automated approaches; AFlow also enables smaller models to outperform GPT-4o on specific tasks at just 4.55% of its inference cost.