遍历即策略：作为外部可验证政策的日志提炼门控行为树，用于安全、稳健和高效的智能体

Autonomous large language model (LLM) agents often fail due to the implicit nature of long-horizon policies embedded in model weights and transcripts, leading to safety measures being applied retroactively. The proposed method, Traversal-as-Policy, distills sandboxed OpenHands execution logs into a single executable Gated Behavior Tree (GBT). Within the coverage of a task, tree traversal is treated as the control policy instead of unconstrained generation. Each node encodes a state-conditioned action macro mined and merged from successful trajectories, with macros linked to unsafe traces attaching deterministic pre-execution gates over structured tool contexts and bounded histories. This ensures that previously rejected unsafe contexts cannot be re-admitted, updated under experience-grounded monotonicity. The approach provides a lightweight control mechanism at runtime, enhancing the safety and efficiency of agents.