HierFlow: Hierarchical Coupled Dual-Space Search for Automatic Agentic Workflow Generation

Agentic AI systems enable LLMs to solve non-trivial tasks through structured workflows, but automatically generating such workflows remains challenging due to the discrete and combinatorial search space. Existing methods often rely on offline search or training, limiting query-level adaptability and incurring substantial data and engineering overhead. We formulate workflow generation as a coupled topology--execution search problem, where the upper-level topology induces subtask-specific code-search domains and lower-level execution feedback can revise the topology itself. Based on this formulation, we propose HierFlow, a training-free hierarchical test-time search framework for automatic agentic workflow generation. HierFlow couples feedback-driven topology refinement with an MCTS-inspired lightweight tree search for execution-level sub-workflow optimization, and uses an adaptive gating mechanism to selectively trigger execution-level search based on estimated necessity. We further provide a coupling-aware analysis characterizing when hierarchical decomposition and proxy-based gating are beneficial and when their advantages may degrade under stronger cross-subtask coupling. Experiments across QA, mathematical reasoning, and code generation benchmarks show that HierFlow achieves strong performance and favorable efficiency--quality trade-offs, outperforming competitive baselines without additional training.