1.5 Chapter Summary

In this chapter, we embarked on an introductory journey to explore agents. Our journey began with the most fundamental questions:

• What are large language model-driven agents? We first clarified their definition and understood that modern agents are entities with capabilities. They are no longer just scripts executing preset programs but decision-makers capable of autonomous reasoning and tool use.
• How do agents work? We delved into the operating mechanism of agent-environment interaction. We learned that this continuous closed loop is the foundation for agents to process information, make decisions, influence the environment, and adjust their behavior based on feedback.
• How to build an agent? This was the practical core of this chapter. Using the "intelligent travel assistant" as an example, we built a complete agent driven by a real LLM.
• What are the mainstream application paradigms of agents? Finally, we cast our vision toward broader application domains. We explored two mainstream agent interaction modes: one is "developer tools" represented by GitHub Copilot and Cursor that enhance human workflows; the other is "autonomous collaborators" represented by frameworks like CrewAI, MetaGPT, and AgentScope that can independently complete high-level goals. We also explained the differences between Workflow and Agent.

Through this chapter's learning, we have established a foundational cognitive framework about agents. So, how did it evolve step by step from its initial conception to the present? In the next chapter, we will explore the development history of agents—a journey to trace back to the origins is about to begin!