农业智能体分层执行，复杂任务自动匹配工具并动态生成。
AgriAgent: Contract-Driven Planning and Capability-Aware Tool Orchestration in Real-World Agriculture
- 简单任务由特定模态代理直接处理，复杂任务通过合约驱动规划。
- 在复杂任务上成功率显著高于传统统一执行的基线模型。
- 适合需要多步执行和故障恢复的真实农业场景研究者使用。
现实农业中的智能代理系统需应对多样任务与多模态输入，从轻量级信息理解到复杂的多步骤执行。然而，现有方法多依赖统一执行范式，在任务复杂度差异大、工具不完整等农业环境中表现不佳。为此，我们提出AgriAgent，一种两级农业智能体框架。该框架基于任务复杂度采用分层执行策略：简单任务由模态特定代理直接推理处理；复杂任务则触发合约驱动规划机制，将任务转化为能力需求，进行能力感知的工具编排与动态工具生成，支持多步可验证执行及故障恢复。实验表明，相比依赖统一执行范式的工具中心型基线，AgriAgent在复杂任务上的执行成功率和鲁棒性显著提升。所有代码与数据将在论文录用后公开，以促进可复现研究。
原文摘要 · Abstract (English)
Intelligent agent systems in real-world agricultural scenarios must handle diverse tasks under multimodal inputs, ranging from lightweight information understanding to complex multi-step execution. However, most existing approaches rely on a unified execution paradigm, which struggles to accommodate large variations in task complexity and incomplete tool availability commonly observed in agricultural environments. To address this challenge, we propose AgriAgent, a two-level agent framework for real-world agriculture. AgriAgent adopts a hierarchical execution strategy based on task complexity: simple tasks are handled through direct reasoning by modality-specific agents, while complex tasks trigger a contract-driven planning mechanism that formulates tasks as capability requirements and performs capability-aware tool orchestration and dynamic tool generation, enabling multi-step and verifiable execution with failure recovery. Experimental results show that AgriAgent achieves higher execution success rates and robustness on complex tasks compared to existing tool-centric agent baselines that rely on unified execution paradigms. All code, data will be released at after our work be accepted to promote reproducible research.
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