Building Enterprise AI Agents

Ebook Description

Building Enterprise AI Agents: From Prototype to Production-Grade LLM Systems is a comprehensive technical and architectural guide for designing, deploying, and governing large language model (LLM)–powered AI agents in real-world enterprise environments.

As generative AI rapidly reshapes how organizations interact with data, automate workflows, and deliver intelligent applications, many teams struggle to move beyond prototypes toward reliable, secure, and scalable production systems. This ebook addresses that gap by providing an end-to-end blueprint for building enterprise-grade AI agents that are grounded in enterprise data, integrated with operational systems, and governed by design.

Starting with foundational concepts in artificial intelligence, generative AI, prompt engineering, and agent theory, the book explains how LLMs reason, where they excel, and where they introduce risks such as hallucinations, cost unpredictability, and integration complexity. It then introduces the core architectural patterns that underpin modern agentic systems, including tool-augmented reasoning, retrieval-augmented generation (RAG), and goal-driven orchestration.

A central focus of the guide is the unified AI development and operations model enabled by the Databricks Data Intelligence Platform. The ebook explores how components such as Mosaic AI, Model Serving, Vector Search, AI SQL Functions, Agent Bricks, and AI/BI work together to support scalable agent orchestration, secure tool access, and enterprise-grade governance through Unity Catalog. Special emphasis is placed on cost management, observability, scalability trade-offs, and operational reliability when running AI systems at scale.

The final chapters examine the rapidly evolving ecosystem of AI agent frameworks and interoperability standards, including LangChain, multi-agent architectures, and the Model Context Protocol (MCP). The book also highlights how MLflow’s ResponsesAgent interface provides a unifying, OpenAI-compatible abstraction that standardizes deployment, evaluation, monitoring, and governance across diverse models and agent frameworks.

Together, these chapters provide a practical, architecture-first roadmap for organizations and practitioners seeking to transition from experimental AI pilots to robust, production-grade AI agents that operate reliably within complex enterprise data environments.

What You’ll Learn

• Core concepts behind generative AI, LLM reasoning, and agent-based system design
• How to design enterprise AI agents using tool-enabled, data-aware, and goal-driven architectures
• Architectural patterns for retrieval-augmented generation (RAG) and agent orchestration at scale
• How the Databricks AI ecosystem supports production-grade LLM applications in a lakehouse-native architecture
• Governance, observability, and cost control strategies for operating AI agents reliably in production
• How Model Context Protocol (MCP) and emerging standards enable interoperable, multi-agent systems
• Practical approaches to deploying, evaluating, and monitoring agents using MLflow and unified serving interfaces

Target Audience

• Enterprise Architects designing AI-native application platforms
• Chief Data, AI, and Analytics Officers responsible for scaling GenAI initiatives
• ML Engineers and MLOps practitioners operationalizing LLM-powered systems
• Data Platform Leaders building unified data, analytics, and AI infrastructures
• Solution Architects and Technical Leads implementing production AI agents
• Strategy and Transformation Leaders driving enterprise automation through AI

Why This Ebook Is Different

Most resources on AI agents focus on experimentation and proof-of-concept development.

This ebook answers a more critical enterprise question:

“How do we design, govern, and operate AI agents reliably at production scale?”

By combining foundational theory, reference architectures, platform-level capabilities, and emerging interoperability standards, this guide delivers a practical, production-oriented blueprint for building enterprise AI agents that are scalable, secure, observable, and aligned with real-world business systems.