通过输入输出逆向还原智能体黑箱的工作流程，实现可解释与可控性。
AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction
- 将智能体工作流重建建模为离散角色与工具调用的组合优化问题。
- 在固定迭代预算下，相比未剪枝搜索，代理相似度更高且消耗令牌更少。
- 无需模型参数即可生成可编辑的白盒流程，适合调试与安全审计场景。
大语言模型在复杂问题求解中展现出强大能力，但许多智能体系统因内部工作流不透明而难以解释与控制。现有框架虽提供显式协作架构，但多数部署的智能体系统仍对用户呈现为黑箱。本文提出智能体工作流重建（AWR）新任务：仅通过输入输出访问，合成一个可解释的近似替代工作流。我们设计AgentXRay，一种基于搜索的框架，将AWR形式化为链式结构工作流空间中的组合优化问题，涉及离散智能体角色与工具调用。不同于模型蒸馏，AgentXRay生成可编辑的白盒工作流，在可观测的输出代理指标下匹配目标输出，且无需访问模型参数。为高效探索庞大搜索空间，采用蒙特卡洛树搜索，并引入基于评分的红黑剪枝机制，动态融合代理质量与搜索深度。跨多个领域实验表明，该方法在固定迭代预算下显著提升代理相似度，降低令牌消耗，支持更深工作流探索。
原文摘要 · Abstract (English)
Large Language Models have shown strong capabilities in complex problem solving, yet many agentic systems remain difficult to interpret and control due to opaque internal workflows. While some frameworks offer explicit architectures for collaboration, many deployed agentic systems operate as black boxes to users. We address this by introducing Agentic Workflow Reconstruction (AWR), a new task aiming to synthesize an explicit, interpretable stand-in workflow that approximates a black-box system using only input-output access. We propose AgentXRay, a search-based framework that formulates AWR as a combinatorial optimization problem over discrete agent roles and tool invocations in a chain-structured workflow space. Unlike model distillation, AgentXRay produces editable white-box workflows that match target outputs under an observable, output-based proxy metric, without accessing model parameters. To navigate the vast search space, AgentXRay employs Monte Carlo Tree Search enhanced by a scoring-based Red-Black Pruning mechanism, which dynamically integrates proxy quality with search depth. Experiments across diverse domains demonstrate that AgentXRay achieves higher proxy similarity and reduces token consumption compared to unpruned search, enabling deeper workflow exploration under fixed iteration budgets.
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