AI Agent Beginner's Guide: How Intelligent Agents Work and the ReAct Framework Explained

From Large Language Models to Agents: A New AI Application Paradigm

The rise of general-purpose agent systems like OpenCloud has made "AI Agent" one of the hottest topics in the tech world. Many businesses are beginning to explore whether they can build highly customized agent systems tailored to their own workflows.

The value of this idea lies in the fact that while general-purpose agents are feature-rich, they struggle to precisely match a company's internal, complex business processes. And to achieve custom development, the first step is to get clear on one thing: what exactly is an agent, and how does it work?

In the simplest terms: an agent is a software system that combines the reasoning capabilities of a large language model (LLM) with real-world tools. It can reason autonomously, select the right tools to execute tasks, and iterate its way to a solution — ultimately accomplishing the goals set by the user.

If you think of an agent as a conscious robot, the LLM is the "brain" — responsible for thinking, reasoning, and planning — while the "hands" that send emails, query databases, search the web, and generate documents are the various tools. Tool usage isn't arbitrary; every decision is controlled by the LLM.

The Essential Difference Between Agents and Traditional AIGC

To understand the value of agents, you first need to distinguish them from traditional AIGC applications.

Before agents came along, using an LLM was straightforward — open a chat interface, ask a question, and the model generates a text response. This is the classic AIGC use case, and its core limitation is that interaction is confined to the text layer.

Here's a concrete example: if you tell a traditional conversational AI, "Book me a window seat on a high-speed train from Beijing to Shanghai tomorrow after 6 PM," it simply can't do it. Traditional LLMs only interact with the world through text generation — they have no ability to execute concrete tasks or deliver real-world results.

The core breakthrough of agents is exactly this: they can interact with the real world by calling tools. Ask it to book a ticket, and it can connect to a ticketing system and complete the purchase. Ask it to analyze data, and it can query a database and generate a report. This ability to go "from thinking to acting" represents a fundamental leap in how we apply AI.

For this reason, the industry widely believes that agents are moving from proof-of-concept to large-scale deployment — and every time a more capable agent solution matures, it triggers a new wave of adoption.

The ReAct Framework: The Core Operating Logic of Agents

Agents can be implemented in many ways, but the dominant paradigm today is the ReAct framework — whether you're building with Dify, developing with LangChain, or using any other platform, the underlying logic is essentially the same.

ReAct stands for Reason + Act, reflecting a loop of "thinking + doing." Its full execution flow can be broken down into the following steps:

The ReAct Iterative Loop

1. Receive user input: The agent receives a question or task instruction from the user.
2. LLM reasoning and decision-making: The LLM analyzes the current instruction and context (including conversation history and tool execution results) to decide which tool to call next.
3. Generate tool invocation instructions: The LLM outputs the specific parameters and instructions for the tool call.
4. Tool executor runs: The agent's tool executor actually runs the tool — which could be a custom-built tool, a third-party API, or another skill.
5. Update context: The tool's output is fed back into the context, providing a new informational foundation for the next round of reasoning.

When the Loop Terminates

After updating the context, the LLM reviews all available information (the full history of instructions and the latest tool results) and performs another round of reasoning to determine whether another tool needs to be called. This cycle of "reason → call tool → update context → reason again" continues iterating.

The stopping condition is also determined by the LLM: when the LLM judges that the information in the current context is sufficient to answer the user's question, it stops calling tools, summarizes the entire process, and outputs a final answer.

This ReAct loop forms the execution backbone of virtually all mainstream agents.

The ReAct framework was formally introduced in 2022 by researchers from Princeton University and Google Research in the paper "ReAct: Synergizing Reasoning and Acting in Language Models." Its core insight is that pure "reasoning chains" (Chain-of-Thought) are prone to hallucinations because the model receives no feedback from the real world, while pure "action" without structured thinking leads to random, inefficient tool usage. ReAct combines both: the model outputs a Thought before each action, then observes an Observation after it, forming the standard Thought → Action → Observation triplet. This mechanism significantly improves model accuracy on multi-step reasoning tasks while making the entire reasoning process traceable and debuggable — providing a foundation for enterprise-grade auditing and compliance requirements.

The Complete Architecture of Enterprise-Grade Agents

At this point, you might be wondering: is the core of an agent really just LLM + tools + ReAct loop?

For conceptual understanding, yes — that's the essence. But for enterprise applications, a production-ready agent system requires several additional critical components:

• Security Sandbox: Provides an isolated, safe execution environment for tools, preventing malicious code from affecting the main system.
• Skill Management: Modularly encapsulates agent capabilities with version control and dynamic scheduling.
• RAG (Retrieval-Augmented Generation): Connects the agent to enterprise knowledge bases, providing domain-specific knowledge support.
• Multi-Agent Collaboration: Multiple specialized agents working in coordination to handle complex business scenarios.
• Task Planning Module: Breaks down complex goals into executable sub-task sequences to improve execution efficiency.

These components mark the critical divide between "prototype validation" and "enterprise deployment." Only by understanding ReAct as the core operating principle can you progressively build a customized agent system that truly meets business needs.

For beginners, mastering the core model of "LLM + tools + ReAct loop" is the single most important first step in getting started with AI Agent development. From understanding the principles to hands-on practice, from simple tool calls to complex task orchestration — it's a clear and well-defined learning path.

RAG (Retrieval-Augmented Generation) is a technical architecture that combines external knowledge bases with the generative capabilities of LLMs. An LLM's parametric knowledge has a training cutoff date and cannot include private enterprise data. RAG addresses this limitation by dynamically retrieving relevant document chunks at inference time and injecting them into the prompt. In enterprise agents, RAG is typically used alongside vector databases (such as Milvus or Pinecone): enterprise documents are chunked and stored as vector embeddings, and when a user asks a question, a semantic similarity search is performed first, with the retrieved content passed to the LLM as context. This enables agents to answer questions about product manuals, internal policies, historical cases, and other information the LLM originally "doesn't know" — making it the core technical pathway for enterprise knowledge base deployment.