Most AI agents break in production. Yours won't.
The difference between a demo and a deployable system? Architecture. Not prompts. Not "better models." The disciplined design that keeps agents reliable when reality gets messy.
This book teaches you to build AI agents as systems, not conversations. You'll learn the complete stack: agent loops, state management, tool contracts, multi-agent teams, guardrails, and production deployment. No theory. No fluff. Just the engineering discipline that separates toys from tools.
What You'll Build:
The Agent Loop (Chapter 2-3): Master the core cycle: input, planning, tool execution, observation, state updates, and artifact generation. Build your first working agent in one evening with draft-first safety patterns and approval gates.
Production Patterns (Chapter 7-12): Implement tool contracts with idempotency, multi-agent teams with verifier roles, and guardrails with permission tiers. Learn when to use deterministic workflows vs. agents, and how to prevent the chaos that kills most agent projects.
Memory & Retrieval (Chapter 8-9): Separate short-term state from long-term knowledge. Build RAG systems that cite sources, resolve conflicts, and prevent hallucinated facts from spreading through your operations.
Deployment Architecture (Chapter 16-18): Scale from single-run agents to event-driven systems. Manage costs, implement observability, and deploy with rollout stages, kill switches, and copyable blueprints.
Who This Is For:
Operators, founders, and technical teams building real automation, not experimenting with prompts. No-code builders and developers both get the complete architecture for agents that ship.
The Result:
An agent operating system: a reusable foundation with consistent governance, measurable performance, and production-ready patterns you can deploy across your business.
Stop building demos. Start building systems that last.
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