BehaviorPilot: Behavior-Guided Data Synthesis for Tool-Using Agents

Training tool-using agents requires diverse interaction data and reliable execution feedback. Recent approaches adopt scenario-centric synthesis, generating environments and tasks across domains and tool configurations. However, scenario diversity does not ensure broad behavioral coverage: tasks from different scenarios may repeatedly exercise the same interaction patterns while leaving others underrepresented. To enable explicit control over behavioral coverage, we introduce tool-use behaviors as a representation of recurring interaction patterns and propose BehaviorPilot, a behavior-guided data synthesis framework. BehaviorPilot integrates three modules: behavior taxonomy induction to build a hierarchical behavioral representation from open-source trajectories for task synthesis and model diagnosis; behavior-conditioned data synthesis to generate executable tasks targeting selected behavior combinations and retain rollouts that pass task verification and cover the target behaviors; and iterative behavior-guided refinement to target model weaknesses by allocating additional synthesis budget in proportion to per-behavior failure rates on a fixed, behavior-balanced validation set. Applied to Qwen3-8B, BehaviorPilot achieves the best benchmark-average scores among the evaluated baselines on VitaBench, BFCL V4 multi-turn, and \tau^2-Bench. Notably, the resulting 8B model surpasses Qwen3-32B in overall performance.