This chapter explores AI agents—autonomous systems that can perceive, reason, and act to achieve goals in complex environments.
What you’ll learn in this chapter:
AI agents represent a paradigm shift from static models to dynamic, goal-oriented systems that can interact with the world. This chapter covers the fundamental concepts, architectural patterns, and training approaches for building effective agent systems.
Foundations
What is an Agent?: Understanding agent characteristics, autonomy, and the distinction from traditional AI systems
Structured Outputs and Tool Calling: How agents interact with external systems through APIs and tools
Architecture and Design
Agent Design Patterns: Common patterns and best practices for building robust agent systems
ReAct (Reasoning and Acting)
Chain-of-Thought and planning strategies
Memory systems and context management
Multi-agent collaboration
Error recovery and safety patterns
Training and Optimization
On Training Agents: Approaches for training agents to perform complex tasks
Instruction tuning for agent capabilities
Reinforcement learning for agent behavior
Learning from feedback and evaluation metrics
Safety, alignment, and robustness considerations
By the end of this chapter, you’ll understand how to design, build, and train AI agents that can autonomously solve complex tasks while maintaining safety and reliability.