- 1. The Agent Harness
- 2. Tool Engineering
- 3. Planning
- 4. Verification Before Commit
- 5. State Machines, Checkpoints and Stop Reasons
- 6. Side Effects
- 7. Long-Running Tickets
- 8. Human Oversight
- 9. Agent Memory
- 10. Context Engineering
- 11. Agentic RAG
- 12. MCP: Hosts, Clients, Servers and Discovery
- 13. Tool Trust
- 14. Multi-Agent Systems
- 15. Agent Failure Engineering
- 16. Agent Evaluation
- 17. Agent Reliability
- Plus: Further Reading, Appendix: Capstone, One Ticket Through Every Boundary
AI Engineering Visualized, Volume 2: Agents
A Visual Guide to Designing Agents That Plan, Act and Recover
This book is 100% completeLast updated on 2026-11-11
Agents that plan, act and recover: the harness around the model. Tools, plans, checks, state, approvals and memory, drawn and built in Python.
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About
About the Book
Your agent worked in the demo. Then it refunded the wrong order, waited forever on a courier, or told a customer "your refund has been processed" when no payment ever went through.
AI Engineering Visualized, Volume 2: Agents is about everything around the model that turns its proposals into safe, finished work. The book calls that the agent harness: the loop, the tool registry, the checks, the state store, the approvals, the memory and the limits. The model is a part you can swap. The harness is what you test, version and ship.
One assistant, carried forward. Volume 1 ended with a support assistant for Northwind Goods, a fictional online store. Volume 2 picks it up and makes it an agent you could trust with real tickets: refunds, damaged items, late deliveries, address changes. Each chapter builds one part of the harness, shows a failure you can trigger, and fixes it.
Visual first. Every idea is a named mental model, such as The Agent Harness, The Identity Chain, Blast Radius, One Operation, One Receipt, The Waiting Ticket or Every Stop Has a Reason, drawn as a doodle with a plain definition next to it.
Real code. Each chapter has short Python examples ("Hands on Keys") and runs on a scripted stand-in model by default, so it costs nothing. Chapters also report what happened when the same code ran against a real local model (llama3.2 through Ollama), including the bugs those runs exposed and how the harness was changed to catch them.
Chapters 1-3 are in the free sample.
Who it's for
Developers who have a working LLM feature or a basic agent loop and now need it to behave on real tasks: backend and full-stack engineers, data engineers, and technical founders. You need to read Python. It helps to have read Volume 1 (especially its agent chapters), but the ideas it relies on are recapped where they are used.
Complete
All 17 chapters are written, and each chapter's explainer video is live. Buy once and get every future revision free.
Part of the AI Engineering Visualized series: Volume 1: Foundations, Volume 2: Agents, Volume 3: RAG & Knowledge Systems and Volume 4: Production AI. Get all four in the Volumes 1–4 bundle.
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Author
About the Author
Prateek Singh
Prateek Singh is the solo technical founder of Ganak AI Labs (https://ganak.ai), an Indian AI SaaS startup that also runs AI workshops for builders. Its product, Cloudeval AI (https://cloudeval.ai), runs pre-deployment reviews for Azure, plus AWS CloudFormation (beta), with evidence-backed reports, through the web, a CLI, GitHub and MCP. Before AI, Prateek worked as an infrastructure and cloud developer and was an active PowerShell community writer. Books and courses
- Learn Windows Subsystem for Linux: A Practical Guide for Developers and IT Professionals (Apress, 2020): https://www.amazon.com/dp/1484260376
- The UX Foundations of Agentic AI Systems: https://leanpub.com/ux-for-ai
- PowerShell to C# and back, with Deepak Singh Dhami: https://leanpub.com/powershell-to-csharp
- Learn C# in 30 Minutes, with Deepak Singh Dhami: https://leanpub.com/learncsharp
- PowerShell Guide to Python: https://leanpub.com/PowerShell-to-Python
- Plain Perception: https://leanpub.com/plain-perception
- Contributor, PS7Now: https://leanpub.com/ps7now
- Contributor, The PowerShell Conference Book: https://leanpub.com/powershell-conference-book
- Course, Learning Python through PowerShell Concepts (Educative): https://www.educative.io/courses/python-powershell-concepts
Find me
- LinkedIn: https://www.linkedin.com/in/prateekkumarsingh/
- Instagram: https://www.instagram.com/mentalhotfix/
- X: https://x.com/mentalhotfix
- YouTube: https://www.youtube.com/@mentalhotfixx
- GitHub: https://github.com/PrateekKumarSingh
- Ganak AI Labs: https://ganak.ai and https://www.linkedin.com/company/ganakailabs/
- Cloudeval AI: https://cloudeval.ai and https://www.linkedin.com/company/cloudeval/
Contents
Table of Contents
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