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Multi-Agent Workflows

Build AI Agent Systems That Automate Your Job

Ayushi Parmar

Learn to build multi-agent AI workflows that automate your job. Master LangChain, CrewAI, and Claude to create production-grade agent crews for coding, content, customer support, and sales automation.

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About

About

About the Book

This book teaches you how to build multi-agent AI workflows using LangChain, CrewAI, AutoGPT-style systems, and Claude.

You'll learn:

- When to use one agent vs. multiple agents

- How to build production-grade agent crews with LangChain and CrewAI

- Real-world workflows: bug-fixing agents, content pipelines, support automation

- Cost optimization, security, and scaling strategies

You'll build:

- A 3-agent blog automation pipeline (research → write → edit)

- A 4-agent bug-fixing crew for code repositories

- A 5-agent customer support system

- And more hands-on projects you can adapt to your own work

Who this book is for:

- Mid-level developers who want to automate repetitive tasks

- Technical PMs exploring AI agent architectures

- Anyone tired of manually chaining ChatGPT prompts and ready to build real workflows

The book includes 12 chapters with complete, runnable Python code examples. Each workflow chapter includes a cheat sheet you can use as a reference.

Chapters 1–2 are available as a free sample.

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Artificial IntelligenceLarge language models

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Author

About the Author

Ayushi Parmar

Aayushi is a technical writer and AI automation expert focused on building practical, production-grade multi-agent workflows. With experience in log analysis, observability tooling, and payments infrastructure, she brings real-world perspective to AI agent design.

Her work bridges the gap between AI research and practical implementation, helping developers and technical PMs understand when and how to deploy multi-agent systems effectively.

When not writing about AI workflows, Aayushi explores digital payments systems and infrastructure automation. She believes the future of work lies in well-designed agent crews, not single monolithic AI systems.

Contents

Table of Contents

Multi-Agent Workflows

  1. Build AI Agent Systems That Automate Your Job
  2. About This Book
  3. How to Read This Book
  4. A Note on Code Examples
  5. What you’ll learn
  6. Problem: Why this matters
  7. Core idea: From “one LLM” to “AI agent team”
  8. What you’ll be able to build
  9. Why this book is different from another “LangChain guide”
  10. How to get the most out of this book
  11. Chapter 1 cheat sheet

Chapter 3 — Single Agent vs Multi-Agent: When to Level Up

  1. What you’ll learn in this chapter
  2. The “one-agent” sweet spot
  3. When a single agent falls short
  4. The multi-agent mindset
  5. When to split into multiple agents
  6. Example: From single agent to multi-agent blog pipeline
  7. When to use LangChain vs CrewAI vs AutoGPT-style
  8. What you’ll learn in this chapter
  9. Before you start: tool setup
  10. The project: Auto-blogging multi-agent pipeline
  11. Step 1 — Define the overall goal
  12. Step 2 — Build the Researcher Agent
  13. Step 3 — Build the Writer Agent
  14. Step 4 — Build the Editor Agent
  15. Step 5 — Orchestrate the multi-agent workflow
  16. Step 6 — Run a sample run
  17. Pitfalls and how to avoid them
  18. How this maps to CrewAI and AutoGPT-style
  19. What you’ll learn in this chapter
  20. From script to crew
  21. AutoGPT-style autonomy: when it helps and when it hurts
  22. Chapter 5 cheat sheet
  23. What you’ll learn in this chapter
  24. The value of coding agents for developers
  25. Project: Multi-agent bug-fixing workflow
  26. Step 1 — Define the top-level goal
  27. Step 2 — Design the agent roles
  28. Step 3 — Build the workflow with CrewAI
  29. Step 4 — How this fits into real-world DevOps
  30. Step 5 — When AutoGPT-style helps (or hurts)
  31. Step 6 — Pitfalls and best practices
  32. Chapter 6 cheat sheet
  33. What you’ll learn in this chapter
  34. The value of agents in content and marketing
  35. Project: Multi-agent content-creation crew
  36. Step 1 — Define the top-level goal
  37. Step 2 — Design the agent roles
  38. Step 3 — Build the crew with CrewAI
  39. Step 4 — Variants and extensions
  40. Step 5 — When AutoGPT-style helps in content
  41. Step 6 — Pitfalls and best practices
  42. Chapter 7 cheat sheet
  43. What you’ll learn in this chapter
  44. The value of agents in customer support and sales
  45. Project: Multi-agent customer-support crew
  46. Step 1 — Define the top-level goal
  47. Step 2 — Design the agent roles
  48. Step 3 — Build the crew with CrewAI
  49. Step 4 — Variants and extensions
  50. Step 5 — When AutoGPT-style helps in support
  51. Step 6 — Pitfalls and best practices
  52. Chapter 8 cheat sheet
  53. What you’ll learn in this chapter
  54. The cost problem in multi-agent systems
  55. Strategy 1 — Model-level cost optimization
  56. Strategy 2 — Prompt and tool optimizations
  57. Strategy 3 — Batching and async patterns
  58. Strategy 4 — Caching and memoization
  59. Strategy 5 — Observability and monitoring
  60. Real-world cost example
  61. Chapter 9 cheat sheet
  62. What you’ll learn in this chapter
  63. The core failure patterns in multi-agent systems
  64. Failure 1 — Hallucinations that cascade across agents
  65. Failure 2 — Coordination failures and stuck agents
  66. Failure 3 — Task duplication and “everyone does everything”
  67. Failure 4 — Loops and retries that never end
  68. Failure 5 — Tool misuse and “wrong tool, wrong place”
  69. Failure 6 — Silent failures and “it looks fine but it’s wrong”
  70. Chapter 10 cheat sheet (troubleshooting playbook)
  71. What you’ll learn in this chapter
  72. The security risks in multi-agent systems
  73. Core security principles for your agents
  74. Pattern 1 — Role-based access control (RBAC) for agents
  75. Pattern 2 — Data privacy and PII handling
  76. Pattern 3 — Secure communication and tool usage
  77. Pattern 4 — Human-in-the-loop and governance agents
  78. Pattern 5 — Logging, observability, and audits
  79. Pattern 6 — Testing and simulating attacks
  80. Chapter 11 cheat sheet (security and governance playbook)
  81. What you’ll learn in this chapter
  82. The shift from “assistants” to “autonomous workflows”
  83. The agentic organization of the future
  84. Where LangChain, CrewAI, and AutoGPT-style fit
  85. Real-world use cases on the horizon
  86. How this changes your career and income
  87. How to stay ahead of the curve
  88. Closing thought

Writing in Markua

  1. Section One
  2. Including a Chapter in the Sample Book
  3. Links
  4. Images
  5. Lists
  6. Page Breaks
  7. Code Samples
  8. Tables
  9. Math
  10. Headings
  11. Block quotes, Asides and Blurbs
  12. Good luck, have fun!

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