فهرست مطالب

• Preface
• Acknowledgments
• About This Book
• About the Author
• About the Cover Illustration
• Chapter 1: Introduction to Agents and Their World
• 1.1 Defining agents
• 1.2 Understanding the component systems of an agent
• 1.3 Examining the rise of the agent era: Why agents?
• 1.4 Peeling back the AI interface
• 1.5 Navigating the agent landscape
• Summary
• Chapter 2: Harnessing the Power of Large Language Models
• 2.1 Mastering the OpenAI API
• 2.2 Exploring open source LLMs with LM Studio
• 2.3 Prompting LLMs with prompt engineering
• 2.4 Choosing the optimal LLM for your specific needs
• 2.5 Exercises
• Summary
• Chapter 3: Engaging GPT Assistants
• 3.1 Exploring GPT assistants through ChatGPT
• 3.2 Building a GPT that can do data science
• 3.3 Customizing a GPT and adding custom actions
• 3.4 Extending an assistant’s knowledge using file uploads
• 3.5 Publishing your GPT
• 3.6 Exercises
• Summary
• Chapter 4: Exploring Multi-Agent Systems
• 4.1 Introducing multi-agent systems with AutoGen Studio
• 4.2 Exploring AutoGen
• 4.3 Group chat with agents and AutoGen
• 4.4 Building an agent crew with CrewAI
• 4.5 Revisiting coding agents with CrewAI
• 4.6 Exercises
• Summary
• Chapter 5: Empowering Agents with Actions
• 5.1 Defining agent actions
• 5.2 Executing OpenAI functions
• 5.3 Introducing Semantic Kernel
• 5.4 Synergizing semantic and native functions
• 5.5 Semantic Kernel as an interactive service agent
• 5.6 Thinking semantically when writing semantic services
• 5.7 Exercises
• Summary
• Chapter 6: Building Autonomous Assistants
• 6.1 Introducing behavior trees
• 6.2 Exploring the GPT Assistants Playground
• 6.3 Introducing agentic behavior trees
• 6.4 Building conversational autonomous multi-agents
• 6.5 Building ABTs with back chaining
• 6.6 Exercises
• Summary
• Chapter 7: Assembling and Using an Agent Platform
• 7.1 Introducing Nexus, not just another agent platform
• 7.2 Introducing Streamlit for chat application development
• 7.3 Developing profiles and personas for agents
• 7.4 Powering the agent and understanding the agent engine
• 7.5 Giving an agent actions and tools
• 7.6 Exercises
• Summary
• Chapter 8: Understanding Agent Memory and Knowledge
• 8.1 Understanding retrieval in AI applications
• 8.2 The basics of retrieval augmented generation (RAG)
• 8.3 Delving into semantic search and document indexing
• 8.4 Constructing RAG with LangChain
• 8.5 Applying RAG to building agent knowledge
• 8.6 Implementing memory in agentic systems
• 8.7 Understanding memory and knowledge compression
• 8.8 Exercises
• Summary
• Chapter 9: Mastering Agent Prompts with Prompt Flow
• 9.1 Why we need systematic prompt engineering
• 9.2 Understanding agent profiles and personas
• 9.3 Setting up your first prompt flow
• 9.4 Evaluating profiles: Rubrics and grounding
• 9.5 Understanding rubrics and grounding
• 9.6 Grounding evaluation with an LLM profile
• 9.7 Comparing profiles: Getting the perfect profile
• Summary
• Chapter 10: Agent Reasoning and Evaluation
• 10.1 Understanding direct solution prompting
• 10.2 Reasoning in prompt engineering
• 10.3 Employing evaluation for consistent solutions
• 10.4 Exercises
• Summary
• Chapter 11: Agent Planning and Feedback
• 11.1 Planning: The essential tool for all agents/assistants
• 11.2 Understanding the sequential planning process
• 11.3 Building a sequential planner
• 11.4 Reviewing a stepwise planner: OpenAI Strawberry
• 11.5 Applying planning, reasoning, evaluation, and feedback to assistant and agentic systems
• 11.6 Exercises
• Summary
• Appendix A: Accessing OpenAI Large Language Models
• A.1 Accessing OpenAI accounts and keys
• A.2 Azure OpenAI Studio, keys, and deployments
• Appendix B: Python Development Environment
• B.1 Downloading the source code
• B.2 Installing Python
• B.3 Installing VS Code
• B.4 Installing VS Code Python extensions
• B.5 Creating a new Python environment with VS Code
• B.6 Using VS Code Dev Containers (Docker)
• Index