AI is fast moving to become an essential tool in the areas the DevOps and Platform Engineering. From large language models (LLMs) like Claude and Gemini, to Retrieval-Augmented Generation (RAG) systems and methods of ensuring that you're feeding an LLM the data it needs (MVP), today's engineers are using AI to move faster and streamline operations. However, AI isn't magic. Models make mistakes (hallucinate), and humans "moving the needle" is still critical.
Agentic AI for Platform Engineering takes a practical, hands-on approach to integrating AIOps into DevOps and Platform Engineering. Starting with Core AIOps concepts before diving into how to apply AIOps to Kubernetes and the Cloud-Native environment, we will move on its applications in observability and testing we will look at MCP (Model Context Protocol) servers which allow LLMs to connect and communicate with external data sources and services, before finishing with how to apply AIOps and DevSecOps to AI generate code and software.
By the end of this book you'll learn how to use AI responsibly to automate common tasks, enhance observability, generate infrastructure as code, and more all while keeping reliability and security top of mind.
You Will Learn:
¿    How to properly use and train Models.
¿    Implement AI Agents and Agentic workflows
¿    When and how to spot hallucinations from Models.
¿    How current tools tie into AIOps
This Book is for: 
Mid-to-senior level software and platform engineers, software developers and product managers who are looking to utilise AI Agents in their DevOps pipelines and workflows to help automate and streamline their processes.