AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction

• URL: http://arxiv.org/abs/2602.05353v2
• Date: Fri, 06 Feb 2026 09:12:16 GMT
• Title: AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction
• Abstract summary: We introduce Agentic Reconstruction (AWR), a new task aiming to synthesize an explicit, interpretable stand-in workflow.<n>We propose AgentXRay, a search-based framework that formulates AWR as a optimization metric over discrete agent roles.<n>We demonstrate that AgentXRay achieves higher proxy similarity and reduces token consumption compared to unpruned search.
• Score: 18.58136299119322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows [5.357463600626556]
  AgentCo-op is a retrieval framework that composes reusable skills, tools, and external agents into executable artifacts.<n>It applies self-guided local repair to implicated components when execution indicates failure.<n>It coordinates specialized agents for spatialomics and gene-set interpretation, and builds a parallel workflow for cross-modality marker analysis on single-cell multiome data.
  arXiv  Detail & Related papers  (2026-05-19T19:22:21Z)
• AgentSPEX: An Agent SPecification and EXecution Language [17.439531520677594]
  We introduce AgentSPEX, an agent framework with explicit control flow and modular structure.<n>AgentSPEX supports typed steps, branching and loops, parallel execution, reusable submodules, and explicit state management.<n>We show through a user study that AgentSPEX provides a more interpretable and accessible workflow-authoring paradigm than a popular existing agent framework.
  arXiv  Detail & Related papers  (2026-04-14T23:16:25Z)
• AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent [57.10083973844841]
  AgentArk is a novel framework to distill multi-agent dynamics into the weights of a single model.<n>We investigate three hierarchical distillation strategies across various models, tasks, scaling, and scenarios.<n>By shifting the burden of computation from inference to training, the distilled models preserve the efficiency of one agent while exhibiting strong reasoning and self-correction performance of multiple agents.
  arXiv  Detail & Related papers  (2026-02-03T19:18:28Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• Beyond Monolithic Architectures: A Multi-Agent Search and Knowledge Optimization Framework for Agentic Search [56.78490647843876]
  Agentic search has emerged as a promising paradigm for complex information seeking by enabling Large Language Models (LLMs) to interleave reasoning with tool use.<n>We propose bfM-ASK, a framework that explicitly decouples agentic search into two complementary roles: Search Behavior Agents, which plan and execute search actions, and Knowledge Management Agents, which aggregate, filter, and maintain a compact internal context.
  arXiv  Detail & Related papers  (2026-01-08T08:13:27Z)
• BOAD: Discovering Hierarchical Software Engineering Agents via Bandit Optimization [41.08366028094234]
  Large language models (LLMs) struggle to generalize to real-world software engineering problems.<n>Existing systems often rely on a single agent to handle the entire workflow-interpreting issues.<n>Motivated by how human engineers decompose complex problems, we propose structuring SWE agents as orchestrators coordinating specialized sub-agents.
  arXiv  Detail & Related papers  (2025-12-29T17:41:11Z)
• Designing Domain-Specific Agents via Hierarchical Task Abstraction Mechanism [61.01709143437043]
  We introduce a novel agent design framework centered on a Hierarchical Task Abstraction Mechanism (HTAM)<n>Specifically, HTAM moves beyond emulating social roles, instead structuring multi-agent systems into a logical hierarchy that mirrors the intrinsic task-dependency graph of a given domain.<n>We instantiate this framework as EarthAgent, a multi-agent system tailored for complex geospatial analysis.
  arXiv  Detail & Related papers  (2025-11-21T12:25:47Z)
• ArchPilot: A Proxy-Guided Multi-Agent Approach for Machine Learning Engineering [18.83090180326138]
  ArchPilot is a multi-agent system that integrates architecture generation, proxy-based evaluation, and adaptive search into a unified framework.<n>It prioritizes high-potential candidates with minimal reliance on expensive full training runs, facilitating efficient ML engineering under limited budgets.
  arXiv  Detail & Related papers  (2025-11-06T02:14:59Z)
• AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering [51.07491603393163]
  tAgent is a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals.<n>By leveraging soft supervision and weighted aggregation of agent outputs, Agent learns principled collaboration schemes that capture the complementary strengths of diverse agents.
  arXiv  Detail & Related papers  (2025-10-06T23:20:49Z)
• InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios [28.65914611521654]
  InfiAgent is a Pyramid-like DAG-based Multi-Agent Framework that can be applied to textbfinfinite scenarios.<n>InfiAgent achieves 9.9% higher performance compared to ADAS (similar auto-generated agent framework)
  arXiv  Detail & Related papers  (2025-09-26T15:44:09Z)
• AgentSwift: Efficient LLM Agent Design via Value-guided Hierarchical Search [58.98450205734779]
  Large language model (LLM) agents have demonstrated strong capabilities across diverse domains.<n>Existing agent search methods suffer from three major limitations.<n>We introduce a comprehensive framework to address these challenges.
  arXiv  Detail & Related papers  (2025-06-06T12:07:23Z)
• AgentSquare: Automatic LLM Agent Search in Modular Design Space [16.659969168343082]
  Large Language Models (LLMs) have led to a rapid growth of agentic systems capable of handling a wide range of complex tasks.<n>We introduce a new research problem: Modularized LLM Agent Search (MoLAS)
  arXiv  Detail & Related papers  (2024-10-08T15:52:42Z)
• ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems [80.69865295743149]
  This work attempts to study using LLM-based agents to design collaborative AI systems autonomously.<n>Based on ComfyBench, we develop ComfyAgent, a framework that empowers agents to autonomously design collaborative AI systems by generating.<n>While ComfyAgent achieves a comparable resolve rate to o1-preview and significantly surpasses other agents on ComfyBench, ComfyAgent has resolved only 15% of creative tasks.
  arXiv  Detail & Related papers  (2024-09-02T17:44:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.