Engineering Agentic AI

is a production-grade engineering guide for building agentic AI systems that operate reliably under real-world business constraints.
This book moves beyond prompt experimentation and prototype agents to address the hard engineering problems that emerge in production: hallucinations, tool misuse, context drift, memory leakage, cost overruns, latency spikes, and governance failures. It presents agentic AI as a systems engineering discipline-one that requires explicit control planes, verifiable retrieval pipelines, bounded execution loops, and human-in-the-loop oversight.
Readers will learn how to design agents as structured, auditable systems composed of goals, state, tools, memory, and termination logic. The book explores verifiable Retrieval-Augmented Generation as a foundational primitive, detailing how grounding, citations, hybrid search, and confidence scoring transform trust from an assumption into an engineered property. Tool contracts are treated as formal interfaces rather than suggestions, with clear patterns for validation, retries, rollback, and safe execution.
Memory is addressed as a first-class architectural concern, covering short-term context, long-term persistence, cross-session identity, and privacy-safe retention strategies. The book also introduces state machines, execution supervisors, and observability frameworks that allow teams to trace agent behavior, diagnose failures, and meet compliance requirements.
Throughout, realistic enterprise case studies illustrate how agentic systems behave under load, during outages, and in regulated environments. Ethical considerations, security risks, and regulatory readiness are integrated directly into system design rather than treated as afterthoughts.
Written in a precise, engineering-driven voice, this book serves as a practical reference for AI engineers, ML architects, platform teams, and technical leaders who need agentic AI systems that can be shipped, monitored, audited, and trusted in production.

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