4.5 Chapter Summary

In this chapter, building on the large language model knowledge mastered in Chapter 3, we coded and implemented three classic industry agent construction paradigms from scratch through "building wheels ourselves": ReAct, Plan-and-Solve, and Reflection. We not only explored their core working principles but also deeply understood their respective advantages, limitations, and applicable scenarios through specific practical cases.

Core Knowledge Review:

1. ReAct: We built a ReAct agent that can interact with the external world. Through the dynamic loop of "thought-action-observation," it successfully used search engines to answer real-time questions that its own knowledge base couldn't cover. Its core advantages lie in environmental adaptability and dynamic error correction capability, making it the first choice for handling exploratory tasks requiring external tool input.
2. Plan-and-Solve: We implemented a Plan-and-Solve agent that plans first then executes, and used it to solve math word problems requiring multi-step reasoning. It decomposes complex tasks into clear steps, then executes them one by one. Its core advantages lie in structure and stability, particularly suitable for handling tasks with determined logical paths and intensive internal reasoning.
3. Reflection (Self-Reflection and Iteration): We built a Reflection agent with self-optimization capabilities. By introducing the "execute-reflect-refine" iterative loop, it successfully optimized an initially inefficient code solution into an algorithmically superior high-performance version. Its core value lies in significantly improving solution quality, suitable for scenarios with extremely high requirements for result accuracy and reliability.

The three paradigms explored in this chapter represent three different strategies for agents to solve problems, as shown in Table 4.1. In practical applications, which one to choose depends on the core requirements of the task:

Table 4.1 Selection Strategy for Different Agent Loops

At this point, we have mastered the core technologies for building individual agents. To transition knowledge and gain deeper insights into practical applications, in the next section we will explore how to use different low-code platforms and lightweight code solutions for building agents.