AgriAgent: Contract-Driven Planning and Capability-Aware Tool Orchestration in Real-World Agriculture
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918285942980608 |
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| author | Yang, Bo Zhang, Yu Chen, Yunkui Feng, Lanfei Xu, Xiao Aierken, Nueraili Li, Shijian |
| author_facet | Yang, Bo Zhang, Yu Chen, Yunkui Feng, Lanfei Xu, Xiao Aierken, Nueraili Li, Shijian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08308 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AgriAgent: Contract-Driven Planning and Capability-Aware Tool Orchestration in Real-World Agriculture Yang, Bo Zhang, Yu Chen, Yunkui Feng, Lanfei Xu, Xiao Aierken, Nueraili Li, Shijian Computation and Language 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. |
| title | AgriAgent: Contract-Driven Planning and Capability-Aware Tool Orchestration in Real-World Agriculture |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.08308 |