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Production -Ready Agentic AI for Data Engineers: Building Safe, Observable, and Reliable AI Agents on Modern Data Platforms
Paperback
Publisher: Independently Published
Published: Jul 21 2026
Pages: 412
Weight: 1.09
Height: 1.02 Width: 6.00 Depth: 9.00
Language: English
Build Enterprise AI Agents with LangGraph, MCP, A2A, RAG, Databricks, and Production Best Practices
Artificial Intelligence is rapidly evolving from simple chatbots to autonomous, intelligent agents capable of planning, reasoning, collaborating, and executing complex workflows. While many books focus on AI concepts or basic prototypes, very few teach you how to build production-ready Agentic AI systems that are secure, scalable, observable, and enterprise-ready.
This book bridges that gap.
Production-Ready Agentic AI for Data Engineers is a practical, hands-on guide designed for data engineers, AI engineers, machine learning engineers, software developers, cloud architects, and technical professionals who want to design, build, deploy, and operate enterprise-grade AI agent systems.
Rather than stopping at proof-of-concept demos, you'll learn the engineering practices required to move Agentic AI into production with confidence.
Inside this book, you'll learn how to:Understand Agentic AI architecture and multi-agent systems
Build intelligent AI agents using LangGraph
Design planning, reasoning, and memory workflows
Implement Retrieval-Augmented Generation (RAG) pipelines
Integrate Model Context Protocol (MCP) into agent ecosystems
Enable Agent-to-Agent (A2A) communication patterns
Build secure enterprise AI applications
Deploy scalable AI workloads on Databricks
Implement observability, tracing, evaluation, and monitoring
Apply guardrails to reduce hallucinations and prompt injection risks
Manage long-term memory, semantic caching, and vector databases
Orchestrate AI workflows with modern engineering patterns
Optimize performance, latency, and operational costs
Design production-ready APIs and deployment architectures
Apply governance, security, compliance, and responsible AI practices
Agentic AI Fundamentals
Enterprise AI Architecture
LangGraph
Multi-Agent Systems
MCP (Model Context Protocol)
Agent-to-Agent (A2A) Communication
Retrieval-Augmented Generation (RAG)
Vector Databases
Semantic Search
Embeddings
Memory Architectures
Tool Calling
Function Calling
Databricks AI Platform
AI Workflows
AI Deployment
Production APIs
Evaluation Frameworks
AI Observability
Logging and Tracing
Security Best Practices
Prompt Engineering
Prompt Injection Defense
Guardrails
Human-in-the-Loop Systems
Responsible AI
Performance Optimization
Scalability
Cloud Deployment
Production Monitoring
This book is ideal for:
Data Engineers
AI Engineers
Machine Learning Engineers
Software Engineers
Data Scientists
Cloud Engineers
MLOps Engineers
Platform Engineers
Solutions Architects
Technical Leads
Engineering Managers
Enterprise AI Teams
Students and professionals transitioning into AI engineering
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