AgriAgent: Contract-Driven Planning and Capability-Aware Tool Orchestration in Real-World Agriculture

TL;DR

AgriAgent introduces a hierarchical, contract-driven agent framework for agriculture that improves task execution success and robustness by dynamically orchestrating tools based on task complexity.

Contribution

It presents a novel two-level agent system with capability-aware planning and dynamic tool generation for real-world agricultural tasks.

Findings

Higher success rates on complex tasks.

Enhanced robustness and failure recovery.

Effective multi-step task execution.

Abstract

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.