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Book synopsis

Most AI tutorials teach you prompts. This book teaches you patterns.



Production AI engineering - the discipline of turning a language model into something reliable, safe, auditable, and shippable - is mostly undocumented. The libraries churn every quarter. The patterns endure.



Agentic AI Harness Pattern distills 15 of those patterns by reading two mature production codebases side by side: Claude Code, Anthropic's TypeScript CLI for agentic coding, and Hermes, a Python agent built to run across messaging platforms. The two systems make different language choices, different concurrency choices, and different deployment choices - but the harness pattern they implement is the same.



Every chapter follows the same rhythm:

• Name the pattern. What problem is the harness solving?


• Show Claude Code's implementation in real TypeScript.


• Show Hermes's implementation in real Python.


• Compare them as a table. Where do they diverge, and why?


• Recommend when to use which. A decision rule, not a hot take.


• Apply the pattern to a defensive cyber-security agent. A worked example that shows the pattern under operational pressure.

Inside the 15 patterns

1. The Harness Paradigm - why a model alone is not a product


2. Tool Architecture and the Tool Contract - the boundary between reasoning and consequence


3. The Query / Agent Loop - what happens between the model's tool call and the next turn


4. Permission Systems and Safety Guardrails - gating the destructive set


5. Tool Orchestration and Execution - partitioning safe vs. serial work


6. Context Management at Scale - the five strategies before compaction


7. Multi-Agent Coordination - when one agent isn't enough


8. Memory Systems and State Persistence - three tiers, one cache


9. Observability and Debugging - distributed tracing for non-deterministic systems


10. Production Deployment Patterns - SDK-first vs. gateway-first


11. Hook / Event-Driven Automation - the layer above the loop


12. The Skill System Pattern - capabilities as content, not code


13. MCP Integration - connecting agents to the world


14. Model Routing and Provider Abstraction - falling back without falling over


15. Structured Output and Schema-Constrained Generation - when free text isn't enough

Who this book is for

• Engineers building AI products who keep hitting the same architectural questions and want vetted answers.


• Architects and tech leads making the build-vs-buy-vs-wrap decision for an agent platform.


• Security and compliance reviewers who need to understand how a production agent enforces a destructive-action gate, an audit trail, and an iteration budget.

Each chapter stands alone. Read what you need; read end-to-end and the patterns compound. Either way, you'll close the book with a working mental model of how to design an AI agent that survives contact with production.

About the authors

Ken Huang is CEO of DistributedApps.ai, advising organizations on production-grade agent deployment at the intersection of AI, distributed systems, and security.



Grace Huang is a Product Manager and AI Engineer at PIMCO, where she ships AI features for the world's largest fixed-income asset manager. Her focus is the engineering rigor that makes AI products trustworthy in regulated environments.



The model is intelligence. The harness is the system.

Start here.

Book details

• Full title: Agentic AI Harness Pattern
• Subtitle: The Top 15 Patterns Learned from Leaked Claude Code and Hermes Agent
• Author: Grace Huang, Ken Huang
• Language: English
• Binding: Paperback
• Number of pages: 288
• EAN: 9798196177972
• ID: 52383053
• Publisher: Independently published
• Weight: 674 g
• Dimensions: 280 × 216 × 15 mm
• Date of publishing: 09. May 2026

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