Systems Thinking for Agentic AI

Apply systems thinking to build production-ready LLM and agentic AI systems using RAG, MCP, tools, memory, guardrails, evaluation, observability, and practical architecture patterns

Key Features

Architect reliable AI agent systems beyond prototypes and demos

Build with RAG, MCP, tool calling, memory, and agent orchestration

Engineer for safety, evaluation, observability, cost, and failure handling

Book Description

Engineering a reliable AI system isn't as easy as calling an API. As AI applications move from prototypes to production, you must solve system-level problems the model alone cannot solve: retrieval quality, tool execution, hallucinations, validation, latency, cost, failures, observability, and operational control.

Systems Thinking for Agentic AI gives engineers and architects a practical framework for tackling these challenges. You ll first understand how LLMs work through tokens, embeddings, and transformers, then learn to control their behavior with prompting, structured outputs, and decoding strategies. You ll connect models to real systems using function calling, tools, and MCP, and build grounded RAG pipelines with embeddings and vector databases.

As you progress, you ll design agentic workflows with planning, memory, orchestration, and controlled execution. You ll implement guardrails, permissions, validation, human approval, evaluation, regression testing, and observability through logs, metrics, and traces. You ll also address production concerns including scaling, performance, cost, timeouts, retries, and fallbacks.

Finally, you ll bring these principles together by building an end-to-end Code Review Agent with Spring Boot, giving you a practical blueprint for engineering AI systems you can confidently operate in production.

What you will learn

Understand how tokens, embeddings, and transformers power LLMs

Control LLM behavior with prompts and structured outputs

Connect AI systems to tools using function calling and MCP

Build grounded RAG pipelines with embeddings and vector databases

Design agents using planning, memory, and orchestration

Apply guardrails, validation, permissions, and human approval

Evaluate and debug AI systems with tests, metrics, and traces

Engineer for latency, cost, scaling, retries, and failures

Who this book is for

This book is for technical leads, software architects, software engineers, and backend developers who want to move beyond AI prototypes and build reliable LLM and agentic AI systems for production. No machine learning background is required. Readers should be comfortable with backend development concepts such as APIs, distributed systems, and system design. Java and Spring Boot developers will also benefit from the end-to-end Code Review Agent implementation.