Language models demo impressively. Engineering them for enterprise production is a different job entirely.
Modern engineering teams face a fundamental shift: systems that are probabilistic, stateful, tool-using, and occasionally wrong in fluent, convincing prose. Building Enterprise Agentic AI Systems is a systems-first guide to designing, building, and operating agentic AI that survives production traffic, security review, compliance, and 2 a.m. incidents - not just demo day.
This is not a prompting book. It treats large language models as black-box probabilistic components inside larger software architectures, and focuses on the part of the job that actually determines whether a system survives contact with production: the control surfaces around the model - context and memory, tool permissions, multi-agent coordination, evaluation, security, and cost.
Who this book is for:
- Engineers and developers moving from prompt experiments to production-grade AI system architecture
- Architects and tech leads designing system boundaries, tool integration, reliability, and governance patterns
- Engineering managers and leaders weighing investment trade-offs, security risk, evaluation pipelines, and operational readiness
What's inside:
- Foundations - how agentic systems differ from deterministic software, and how to think in terms of blast radius, not just correctness
- Building blocks - context engineering and memory, tool calling (including MCP), planning and reflection, and multi-agent architecture done with restraint instead of hype
- Enterprise engineering - reliability and evaluation, performance and cost engineering, security and compliance (prompt injection, data leakage, governance), and what it actually takes to deploy and operate these systems at scale
- Advanced topics - fine-tuning versus prompting, and a practical map of today's AI engineering ecosystem
- Hands-on builds - full end-to-end walkthroughs of an enterprise assistant, a research agent, and a production-readiness checklist you can apply to your own systems
Every chapter opens with a real engineering problem, not a buzzword, and pairs the concept with working code across LangGraph, Microsoft Agent Framework, and CrewAI - so the ideas are never abstract for long.
By the end, you won't just know how to build an agent that works in a demo. You'll know when to reach for full autonomy versus a deterministic workflow, how to defend a tool-using pipeline against the failure modes that only show up in production, and how to move a prototype to something you can trust - and explain - in front of a compliance review.
If you're responsible for AI systems that have to actually work, not just impress a room, this book is for you.
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