Made
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AI Agents

Made Crystal Clear

A practical, visual guide to building agents that survive real users

Everyone is being told to build agents. Almost nobody is told what an agent actually is, how its loop really works, or why the demo that dazzled on Friday falls apart the moment a real user touches it on Monday. This guide takes you from zero to an agent you can trust: the loop, tools, context and memory, guardrails, evaluation, cost, and the honest list of jobs an agent should never be given. Patterns first, frameworks second, so what you learn outlives the next SDK release.

Pages
223
Level
Beginner to advanced
Status
Available
Kindle · $12.99 ↗Paperback · $37.99 ↗Give feedback

Inside the book

  1. 01The Agent Moment, and the Gap Nobody Mentions
  2. 02What an Agent Actually Is
  3. 03When NOT to Build an Agent
  4. 04Your First Agent, From Scratch
  5. 05Tools, the Agent's Hands
  6. 06Context and Memory
  7. 07MCP, Plugging In the World
  8. 08The Production Cliff
  9. 09Guardrails, Budgets and Stop Conditions
  10. 10The Trust Boundary
  11. 11Proving It Works: Evals
  12. 12Debugging a Failed Run
  13. 13The Money Chapter
  14. 14Frameworks, Multi-Agent, and What to Learn Next

You'll leave with

  • Understand the agent loop: goal, context, decision, tool call, observation, stop
  • Ship your first working agent, then make it fail on purpose to see why
  • Tell an agent from a workflow, and know when not to build one at all

For teams

Put AI Agents on every desk at your company.

One licence, every book in the series, one link for everyone. From $250 a year.

See the team licences

Sources

Every source cited in AI Agents Made Crystal Clear. Each number matches the [n] marker next to that link in the book's "Go deeper" lists, so you can trace any claim back to its origin.

  1. [1]Building effective agents (Anthropic Engineering)
  2. [2]12-Factor Agents (HumanLayer)
  3. [3]Production-ready AI agents: 5 lessons from refactoring a monolith (Google Developers, 21 April 2026)
  4. [4]A practical guide to building agents (OpenAI, PDF, 33 pages)
  5. [5]The agent loop (Claude Agent SDK docs)
  6. [6]Claude Agent SDK overview
  7. [7]Running agents (OpenAI Agents SDK)
  8. [8]OpenAI Agents SDK overview
  9. [9]r/AI_Agents
  10. [10]Tool use overview (Anthropic)
  11. [11]Defining tools (Anthropic)
  12. [12]Agents (OpenAI Agents SDK)
  13. [13]Writing tools for agents (Anthropic Engineering)
  14. [14]OpenAI Agents SDK: tools
  15. [15]Context Rot: How Increasing Input Tokens Impacts LLM Performance (Chroma, 14 July 2025)
  16. [16]Effective context engineering for AI agents (Anthropic, 29 September 2025)
  17. [17]Claude Agent SDK: subagents
  18. [18]Prompt caching (Anthropic)
  19. [19]Architecture overview (modelcontextprotocol.io)
  20. [20]Architecture, specification revision 2026-07-28
  21. [21]Server concepts
  22. [22]Tools, specification revision 2026-07-28
  23. [23]Security best practices, revision 2026-07-28
  24. [24]Security best practices, revision 2025-11-25
  25. [25]Specification index
  26. [26]How we built our multi-agent research system (Anthropic, 13 June 2025)
  27. [27]Don't build multi-agents (Cognition, 12 June 2025)
  28. [28]Permissions (Claude Agent SDK)
  29. [29]Guardrails (OpenAI Agents SDK)
  30. [30]Pricing (Anthropic)
  31. [31]OWASP LLM01:2025 Prompt Injection
  32. [32]OWASP Top 10 for LLM Applications 2025 (PDF)
  33. [33]OWASP Securing Agentic Applications Guide 1.0
  34. [34]The lethal trifecta for AI agents (Simon Willison, 16 June 2025)
  35. [35]The Dual LLM pattern (Simon Willison, 25 April 2023)
  36. [36]Design patterns for securing LLM agents (Willison on Beurer-Kellner et al., 13 June 2025)
  37. [37]Willison's prompt-injection tag
  38. [38]Mitigating the risk of prompt injections in browser use (Anthropic, 24 November 2025)
  39. [39]Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection (arXiv:2302.12173)
  40. [40]Demystifying evals for AI agents (Anthropic Engineering, 9 January 2026)
  41. [41]tau-bench (17 June 2024)
  42. [42]tau2-bench: Evaluating Conversational Agents in a Dual-Control Environment (9 June 2025)
  43. [43]sierra-research/tau2-bench (GitHub)
  44. [44]SWE-bench: Can Language Models Resolve Real-World GitHub Issues? (October 2023)
  45. [45]What skills does SWE-bench Verified evaluate? (Epoch AI, 13 June 2025)
  46. [46]SWE-bench Verified harness details (Epoch AI)
  47. [47]GenAI spans, OpenTelemetry semantic conventions
  48. [48]GenAI agent spans
  49. [49]GenAI metrics
  50. [50]Inside the LLM Call: GenAI Observability with OpenTelemetry
  51. [51]Tracing, OpenAI Agents SDK
  52. [52]OpenAI API pricing (official)
  53. [53]Google Gemini API pricing (official)
  54. [54]LangChain pricing
  55. [55]n8n pricing
  56. [56]Zapier pricing
  57. [57]LangGraph overview
  58. [58]LangGraph persistence
  59. [59]CrewAI crews
  60. [60]CrewAI flows
  61. [61]Pydantic AI overview
  62. [62]Mastra agents

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