Intelligent AI Automation with Large Language Models: Building AI Agents, Workflow Orchestration Pipelines, and Autonomous Systems for Real-World Applications. - Tapa blanda

Sinopsis

Modern software is no longer just programmed, it is orchestrated.
As large language models evolve from simple text generators into reasoning engines, a new engineering discipline has emerged: Intelligent Automation with Large Language Models. This book is your practical, end-to-end guide to designing automation systems that can think, plan, and act autonomously across real-world environments.
Intelligent Automation with Large Language Models shows you how to move beyond isolated prompts and experiments to build AI agents, workflow orchestration pipelines, and autonomous systems that integrate seamlessly with APIs, business tools, and production infrastructure. You’ll learn how to engineer intelligent workflows where LLMs don’t just respond, they coordinate, reason, and make decisions within structured systems.
Bridging theory and hands-on implementation, this book walks you through the core building blocks of modern AI automation: agent design, context management, workflow orchestration, memory systems, and multi-step decision pipelines. Each concept is grounded in practical architectures that can be adapted to real applications, from document processing and research automation to business operations and internal tooling.
Rather than focusing on hype or fragile one-off solutions, this guide emphasizes engineering discipline: scalability, reliability, cost control, monitoring, and responsible deployment. You’ll learn how to design automation systems that are not only powerful, but maintainable, observable, and production-ready.
What You’ll Learn

1. Core principles of intelligent automation and LLM-driven system design
2. How to build and coordinate AI agents for multi-step tasks
3. Designing workflow orchestration pipelines that connect LLMs, APIs, and tools
4. Context and memory strategies for long-running and stateful AI systems
5. Patterns for autonomous decision-making and agent collaboration
6. Best practices for scaling, monitoring, and governing AI automation in production
7. Real-world architectures for business, research, and operational automation

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