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[2602.18968] Robust and Efficient Tool Orchestration via Layered Execution Structures with Reflective Correction
Summary
This article presents a novel approach to tool orchestration in agentic systems, emphasizing a layered execution structure that enhances robustness and efficiency while minimizing execution overhead.
Why It Matters
The research addresses critical challenges in tool orchestration, which is essential for the effective functioning of AI systems. By introducing a reflective correction mechanism, the authors provide a solution that not only improves execution reliability but also reduces complexity, making it relevant for developers and researchers in AI.
Key Takeaways
- Tool orchestration can be simplified using a layered execution structure.
- Local error correction during execution enhances robustness.
- The proposed method reduces execution complexity and overhead.
- Effective orchestration does not require detailed dependency graphs.
- The approach is applicable to various agentic systems.
Computer Science > Artificial Intelligence arXiv:2602.18968 (cs) [Submitted on 21 Feb 2026] Title:Robust and Efficient Tool Orchestration via Layered Execution Structures with Reflective Correction Authors:Tao Zhe, Haoyu Wang, Bo Luo, Min Wu, Wei Fan, Xiao Luo, Zijun Yao, Haifeng Chen, Dongjie Wang View a PDF of the paper titled Robust and Efficient Tool Orchestration via Layered Execution Structures with Reflective Correction, by Tao Zhe and 8 other authors View PDF HTML (experimental) Abstract:Tool invocation is a core capability of agentic systems, yet failures often arise not from individual tool calls but from how multiple tools are organized and executed together. Existing approaches tightly couple tool execution with stepwise language reasoning or explicit planning, leading to brittle behavior and high execution overhead. To overcome these limitations, we revisit tool invocation from the perspective of tool orchestration. Our key insight is that effective orchestration does not require precise dependency graphs or fine-grained planning. Instead, a coarse-grained layer structure suffices to provide global guidance, while execution-time errors can be corrected locally. Specifically, we model tool orchestration as learning a layered execution structure that captures high-level tool dependencies, inducing layer-wise execution through context constraints. To handle execution-time failures, we introduce a schema-aware reflective correction mechanism that detects and repai...
