Agentic AI for DevOps Engineers

Description

Agentic AI for DevOps is a practical, hands-on guide to building autonomous DevOps workflows powered by AI agents, LLMs, and intelligent automation systems. The book shows you how to modernize software delivery and cloud operations by integrating AI into CI/CD pipelines, infrastructure automation, observability, incident response, and platform engineering.

You’ll learn how to design AI-driven workflows that can analyze logs, automate deployments, optimize infrastructure, remediate failures, and improve operational efficiency with minimal human intervention. Through real-world projects and demonstrations, the book explores the use of AI copilots, orchestration frameworks, cloud-native tooling, and DevOps platforms to create scalable and production-ready autonomous systems. The book also covers prompt engineering, AI workflow orchestration, security, governance, and best practices for building reliable AI-powered DevOps environments.

By the end of the book, you’ll be able to confidently build, deploy, and manage AI-driven DevOps systems that improve speed, reliability, scalability, and operational efficiency.

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What you will learn

• Build a practical foundation in Generative AI for DevOps
• Apply AI across GitHub, Azure DevOps, and CI/CD workflows
• Use AI to improve automation, infrastructure, and operational efficiency
• Implement AI with guardrails, governance, and enterprise awareness
• Evaluate agentic AI for advanced DevOps scenarios
• Understand how agentic AI, tools, memory, and MCP extend DevOps automation
• Use AI to improve code, pipelines, infrastructure as code, and documentation

Who this book is for

This book is designed for DevOps engineers, platform engineers, SREs, cloud architects, automation specialists, and software developers who want to leverage Agentic AI to automate and optimize modern DevOps workflows. It is ideal for professionals looking to build autonomous CI/CD pipelines, AI-driven infrastructure automation, intelligent monitoring systems, self-healing operations, and scalable cloud-native workflows using AI agents, LLMs, and modern DevOps platforms.

Table of Contents

1. Generative AI Concepts for DevOps
2. Practical Applications of Generative AI in DevOps
3. DevOps AI Tools, Governance, and Enterprise Readiness
4. Integrating Generative AI Into DevOps Pipelines
5. Agentic AI for CI/CD Failure Triage and PR Quality Assessment
6. Agentic AI for Advanced DevOps Scenarios: Memory and MCP
7. Multi-Agent Incident Response and Agent Observability
8. Conclusion and Next Steps: Applying Generative AI to DevOps Responsibly