目标
Agents are designed to extend language model capabilities to solve real-world challenges. Successful implementations require robust problem-solving capabilities enabling agents to perform well on novel tasks. To solve real-world problems effectively, agents require the ability to reason and plan as well as call tools that interact with an external environment. In this section we explore why reasoning, planning, and tool calling are critical to agent success. 代理旨在扩展语言模型功能以解决现实世界的挑战。成功的实施需要强大的解决问题的能力，使代理能够在新任务上表现良好。为了有效地解决现实世界的问题，代理需要具有推理和计划以及调用与外部环境交互的工具的能力。 The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey
什么是 Agent
Agent 基于 LLM 解决复杂问题一些策略。
一个 Agent 常常被赋予一个角色。
Agent 的能力
- 推理增强。
- 规划。
- 工具执行。