Agentic AI for Platform Engineering
Building Intelligent Autonomous Systems and Beyond
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Beschreibung
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 MPC (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.
• Understand how to Implement AI Agents and Agentic workflows
• Know when and how to spot hallucinations from Models.
• See how current tools tie into AIOps
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.
Learn how to integrate AI OPs and agents into your DevOps and Platform worksflows Understand how to check and fix GenAI mistakes in the code and processses Explore how to streamline, automate and move faster with AI tools
Autor*in
Michael Levan
Themen in »Agentic AI for Platform Engineering«
DevOps AIOps AppSecOps AI in Platform Engineering LLMs for IaC AI assisted CI/CD pipelines Kubernetes MCPs Site Reliability Engineering Model Context Protocols APIs AI-driven Observability Tool