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AI :: Agent :: Training :: Principles of Building AI Agents
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readme.md

AI :: Agent :: Training :: Principles of Building AI Agents

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Principles of Building AI Agents

Cover

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Title

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Foreward

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1st Edition

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Signature

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Introduction

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viii

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Part I — Prompting a Large Language Model (LLM)

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1. A brief history of LLMs

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2. Choosing a Provider and Model

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3. Writing Great Prompts

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Part II — Building an Agent

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4. Agents 101

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5. Model Routing and Structured Output

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6. Tool Calling

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7. Agent Memory

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8. Dynamic Agents

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9. Agent Middleware

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Part III — Tools & MCP

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10. Popular Third-Party Tools

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11. Model Context Protocol (MCP): Connecting Agents and Tools

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Part IV — Graph-Based Workflows

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12. Workflows 101

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13. Branching, Chaining, Merging, Conditions

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14. Suspend and Resume

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15. Streaming Updates

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16. Observability and Tracing

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Part V — Retrieval-Augmented Generation (RAG)

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17. RAG 101

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18. Choosing a Vector Database

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19. Setting Up Your RAG Pipeline

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20. Alternatives To RAG

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Part VI — Multi-Agent Systems

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21. Multi-Agent 101

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22. Agent Supervisor

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23. Control Flow

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24. Workflows As Tools

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25. Combining The Patterns

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26. Multi-Agent Standards

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Part VII — Evals

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27. Evals 101

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28. Textual Evals

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29. Other Evals

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Part VIII — Development & Deployment

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30. Local Development

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31. Deployment

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Part IX — Everything Else

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32. Multimodal

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33. Code Generation

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34. What's Next

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