智慧农业中的智能体：架构、应用与未来挑战

1. Introduction

With the rapid development of artificial intelligence, large language models, multimodal foundation models, and agent technologies, agricultural intelligent agents are gradually emerging as an important new direction in smart agriculture. Unlike conventional agricultural AI applications that are mainly designed for single tasks such as recognition, prediction, or question answering, agricultural intelligent agents provide a new system-level paradigm for agricultural knowledge understanding, complex task organization, and decision support. In recent years, research on agricultural foundation models has continuously expanded the application scope of foundation models in agricultural knowledge understanding, multimodal information processing, and complex decision support [1,2]. Meanwhile, rapid advances in LLM-based agents, particularly in task planning, tool use, memory management, and feedback-driven refinement, have provided an important methodological foundation for agricultural intelligent agents to evolve from “generative responses” toward “task-driven workflow orchestration” [3,4]. Driven by these advances, agricultural intelligent agents are expected to promote the transition of smart agriculture from isolated model applications toward task-oriented knowledge organization, tool orchestration, and intelligent decision-making.

The motivation for writing this paper stems from the urgent need for efficient, intelligent, and sustainable decision-support systems in agricultural production. With global population growth, the intensifying impacts of climate change, and increasingly severe constraints on agricultural resources and the environment, agricultural production systems are facing increasing demands in terms of production efficiency, resource utilization, environmental sustainability, and management complexity [5,6]. In this context, agricultural intelligent agents provide a new technical pathway for integrating agricultural domain knowledge, dynamic environmental information, external analytical tools, and management decisions, thereby offering new possibilities for improving the organizational capacity and intelligent service capacity of agricultural production. However, existing studies have mainly focused on agricultural foundation models, task-specific agricultural AI applications, or general-purpose LLM-based agents, while a systematic review of LLM-driven agricultural intelligent agents in smart agriculture remains limited. Accordingly, this paper reviews modern agricultural intelligent agents in the context of smart agriculture, with a particular focus on large language model-driven agricultural intelligent agent systems that are capable of knowledge integration, tool use, task organization, and collaborative decision-making.

The literature discussed in this review was organized around the research boundary of modern agricultural intelligent agents. Priority was given to studies that connect agricultural tasks with foundation-model-driven agent capabilities, such as task-oriented reasoning, external resource use, workflow organization, or collaborative decision-making. Related studies on traditional agents, agricultural AI models, foundation models, and retrieval-augmented systems were also considered when they helped clarify conceptual boundaries or provide methodological context.

Specifically, this paper first traces the evolution of the agent concept and defines the core characteristics and research boundaries of agricultural intelligent agents. It then summarizes the basic architectures and key capabilities of modern agents. Subsequently, using agricultural task chains as the main analytical thread, this paper reviews recent research progress on agricultural intelligent agents across representative agricultural scenarios. To avoid treating heterogeneous prototypes as equivalent examples of agentic systems, this review further develops an assessment matrix based on evidence reported in the original studies, distinguishing demonstrated capabilities from dimensions that remain unevaluated. Finally, the paper discusses the key challenges faced by agricultural intelligent agents in real-world agricultural applications and outlines future research directions. This review aims to provide a clear conceptual foundation, technical trajectory, and evidence-based analytical framework for agricultural intelligent agent research, while offering guidance for future development and application in agriculture-related fields.

2. Overview of Agents

Since the mid-1990s, agents have been a prominent topic in computer science and artificial intelligence. The core concern of this field is how systems can exhibit autonomy, goal-oriented behavior, and interactive capabilities within a given environment [7]. Along this line, agent-based computing, software agents, multi-agent systems, and agent-based modeling have developed as related but distinct branches of traditional agent research [8]. In recent years, with the increasing capabilities of large language models in knowledge-based problem solving, complex reasoning, task planning, and tool use, agent research has been undergoing a new paradigm shift. The focus is gradually expanding from rule-driven mechanisms and interactions within closed environments to intelligent execution systems that use large language models as cognitive cores and are capable of multi-step planning, external tool invocation, process feedback, and dynamic refinement around specific goals [3].

For smart agriculture, this paradigm shift is of particular significance. Agricultural tasks are often characterized by the coupling of multi-source information, dynamic external environments, complex knowledge dependencies, and continuous decision-making processes. Standalone models, static knowledge-based question-answering systems, or closed rule-based systems are often insufficient to support complete task chains in real-world agricultural production. Therefore, before discussing agricultural intelligent agents, it is necessary to first review the concept and development of agents, and then further define the core characteristics and research boundaries of agricultural intelligent agents.

2.1. Concept and Development of Agents

Traditional agents do not refer to a single technical form, but rather represent a research paradigm that has gradually developed around autonomous behavior, environmental interaction, and coordination in complex systems. From the perspective of research focus, early agent studies can be broadly grouped into three representative directions. The first direction focuses on individual behavior and cognitive mechanisms, including reactive agents that emphasize stimulus–response behaviors in the environment, BDI agents based on the Belief–Desire–Intention architecture, and interface agents designed to assist users in specific tasks [9,10,11]. The second direction concerns Multi-Agent Systems (MAS), which focus on communication, negotiation, cooperation, and distributed decision-making among multiple autonomous entities in a shared environment [8]. The third direction is Agent-Based Modeling (ABM), which treats agents as simulation units with behavioral rules and analyzes the evolution and emergent phenomena of macroscopic systems by modeling individual decision-making and local interactions [12]. Therefore, the core value of traditional agents lies mainly in rule-based autonomy, structured decision-making, distributed coordination, and complex system modeling. Their implementation typically depends on manually designed state representations, behavioral logic, and interaction mechanisms.

In contrast, the recent emergence of LLM-based agents has promoted the evolution of agents from “autonomous units under predefined rules” to “foundation model-driven systems for open-ended task execution”. ReAct organizes reasoning and acting in an interleaved manner, enabling models to continuously advance tasks through a “thought–action–observation” loop and revise subsequent steps according to external feedback [13]. Toolformer further demonstrates that language models can learn when to call external tools, how to pass parameters, and how to incorporate tool-returned results, thereby overcoming the limitations of completing tasks solely based on parametric knowledge [14]. Thus, the key difference between modern LLM-based agents and traditional agents is not merely the change of the underlying model, but a transformation in the way intelligence is organized. The former uses large language models or foundation models as cognitive cores and emphasizes open-ended semantic understanding, multi-step task planning, external tool use, and feedback-driven process refinement. The latter, by contrast, relies more heavily on explicit rules, predefined protocols, or simulation mechanisms to achieve autonomy, coordination, or modeling within relatively closed task boundaries. Therefore, the term “agent” discussed in the following sections of this paper mainly follows the meaning of modern LLM-based or foundation model-driven agentic AI.

The conceptual distinctions among these agent-related paradigms are summarized in Table 1.

Table 1. Conceptual distinctions among agent-related paradigms discussed in this review.

2.2. Core Characteristics and Research Boundaries of Agricultural Intelligent Agents

Agricultural intelligent agents in this review are defined in a relatively restrictive sense. They refer to foundation-model-driven systems that can organize agricultural objectives, domain knowledge, dynamic data, external tools, and feedback into a task-oriented process. This definition uses a narrower boundary than the loose use of the term “agent” in some agricultural AI studies. Providing a conversational interface, incorporating a large language model, or retrieving agricultural documents before answer generation is not sufficient for a system to be classified as an agricultural intelligent agent. What matters is whether the system can advance an agricultural task through planning, tool invocation, workflow organization, feedback, verification, or collaborative decision-making.

This boundary is necessary because several adjacent paradigms are easily conflated in current agricultural AI research. Agricultural foundation models may provide domain knowledge understanding, text generation, or multimodal representation capabilities, but they do not automatically become agent systems if they lack explicit task planning, external resource use, or process-level interaction. Similarly, a RAG-based agricultural question-answering system can improve the evidential basis and domain specificity of responses, but it remains closer to a knowledge-enhanced advisory system if it only completes a “retrieval–generation” pipeline without forming an action chain toward a task objective [15,16]. Traditional agricultural expert systems, MAS, and ABM also differ from the modern agricultural intelligent agents discussed here, because their autonomy is usually grounded in predefined rules, interaction protocols, or simulation assumptions rather than foundation-model-driven reasoning and open-ended tool use.

Existing studies show that agricultural intelligent agents differ substantially in the depth of their agentic mechanisms. ShizishanGPT integrates large language models, search modules, agricultural knowledge graphs, RAG, and agricultural tools, and therefore represents a transition from ordinary agricultural question answering toward tool-augmented agricultural knowledge services [17]. CottonBot is more directly connected to field-level decision-making because it incorporates soil moisture sensors and weather forecast interfaces into an agentic workflow for cotton irrigation recommendation [18]. AgriWorld provides a stronger form of agenticity by allowing LLM agents to call geospatial queries, remote-sensing analysis, crop simulation, and code execution tools, and to revise reasoning on the basis of execution results [19]. These examples should not be treated as equivalent. Rather, they indicate a spectrum from tool-augmented advisory systems to verifiable task-execution systems.

Based on this boundary, agricultural intelligent agents generally involve four core characteristics. First, they are oriented toward explicit agricultural task objectives, such as advisory support, disease diagnosis, irrigation scheduling, phenotyping workflow construction, farm recordkeeping, or mission planning. Second, they use large language models, multimodal foundation models, or related foundation-model components as important cognitive, reasoning, or interaction cores. Third, they are able to access or coordinate external resources, including knowledge bases, domain-specific models, sensor streams, weather APIs, code execution environments, crop models, digital twins, farm databases, or equipment interfaces. Fourth, they possess some form of multi-step process organization, such as task decomposition, workflow orchestration, intermediate result interpretation, feedback-based refinement, or multi-agent collaboration.

It should be emphasized that these characteristics are not merely a checklist of technical components. The same component may indicate different levels of agenticity depending on how it is embedded in the system. For example, RAG can support ordinary knowledge retrieval, but it becomes more agent-relevant when retrieval is selectively triggered, compared with other information sources, and used to revise downstream reasoning. Likewise, external APIs or domain models do not necessarily make a system agentic unless the system can select, invoke, interpret, and adjust their use according to task requirements. Feedback and verification mechanisms represent a stronger level of agentic capability because they allow the system to reassess intermediate outputs rather than simply generate a final response.

Accordingly, the agricultural intelligent agents reviewed in the following sections mainly refer to systems that satisfy three minimum conditions: they are oriented toward specific agricultural tasks; they use foundation models as important reasoning, interaction, or orchestration components; and they connect knowledge, data, tools, workflows, feedback, or collaboration mechanisms in a way that supports task progression rather than isolated response generation. This definition provides the basis for distinguishing agricultural intelligent agents from standalone agricultural foundation models, static RAG-based question-answering systems, conventional agricultural AI models, and traditional MAS/ABM approaches. It should be noted, however, that the boundary criteria in Table 2 define the inclusion scope of this review rather than the maturity level of individual systems. The evidence reported in the original studies and the deployment readiness of representative systems are therefore further examined after the application synthesis in Section 4.5.

Table 2. Research boundary and analytical criteria for agricultural intelligent agents in this review.

3. Basic Architectures and Key Capabilities of Agents

After clarifying the conceptual boundaries of agents and agricultural intelligent agents in Section 2, it is necessary to further discuss the internal structures and key mechanisms through which modern agents execute tasks. Unlike traditional single-turn generative models, modern large language model-driven agents are increasingly designed as composite systems consisting of multiple components, including goal setting, state and context perception, memory management, task planning, tool use, action execution, and feedback-based refinement. Existing studies generally suggest that memory, planning, and tool use constitute the core capability dimensions of LLM-based agents [3,4,20]. On this basis, this section reviews the key methodological foundations of modern agents from three perspectives: basic architectures, task planning and tool-based behavioral loops, and multi-agent collaboration and reliability evaluation.

3.1. Basic Architectures of Agents

Based on existing representative systems, the architectural design of modern LLM-based agents has gradually shifted from a single mode in which large language models directly respond to inputs toward composite systems supported by task objectives, contextual states, memory mechanisms, decision-making processes, and action interfaces. Generative Agents [21] enable agents to retain past experiences, derive higher-level reflections, and generate subsequent behaviors through a “memory stream–reflection–planning” mechanism. MemGPT [22] introduces an operating system-like hierarchical memory management approach, improving the ability of agents to maintain persistent states in long-range conversations and complex document-based tasks through main context, external storage, and control-flow scheduling. These studies indicate that the basic architecture of modern agents is not merely an external encapsulation of large language models, but an effort to construct stable internal states and behavioral organization mechanisms for continuous task execution.

The basic architecture of LLM-based agents can generally be summarized into five interrelated functional units. The first is the goal and task representation module, which receives user intentions, environmental objectives, or system constraints and transforms them into task descriptions for subsequent processing. The second unit concerns perception and context construction, drawing together user inputs, external observations, historical information, and tool-returned results as the current-state basis for reasoning. The third unit is memory management, which retains short-term task states, longer-term experiences,