AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction
📰 ArXiv cs.AI
Learn to white-box agentic systems using AgentXRay and workflow reconstruction for better interpretability and control
Action Steps
- Apply AgentXRay to an existing agentic system to reconstruct its workflow
- Use the reconstructed workflow to identify potential bottlenecks and areas for improvement
- Configure the system to provide more explicit and interpretable outputs
- Test the modified system to evaluate its performance and transparency
- Compare the results with the original black-box system to assess the benefits of white-boxing
Who Needs to Know This
AI researchers and engineers working with large language models and agentic systems can benefit from this technique to improve transparency and understanding of their models
Key Insight
💡 AgentXRay enables the reconstruction of explicit and interpretable workflows for agentic systems, improving their transparency and controllability
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🔍 Introducing AgentXRay: a technique to white-box agentic systems via workflow reconstruction for better interpretability and control #AI #AgenticSystems
Key Takeaways
Learn to white-box agentic systems using AgentXRay and workflow reconstruction for better interpretability and control
Full Article
Title: AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction
Abstract:
arXiv:2602.05353v3 Announce Type: replace 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 stan
Abstract:
arXiv:2602.05353v3 Announce Type: replace 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 stan
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