AI Security

Reading time: ~45 min | Audience: platform leads, principal engineers, AI architects, security teams | Primary goal: build agent systems that stay stable under real enterprise pressure Preface: Why I Wrote This Version Quick context on why this exists. I have read too many agent posts that sound convincing and then collapse in real enterprise environments. Usually they miss one of three things: They stop at demos. They show code without operations. They draw architecture without incident behavior. This version is for teams that already shipped something and now need clear answers in design review, security review, and on-call: ...

Audio Version Your browser does not support the audio element. Play or download the audio overview Executive Summary In 2026, software teams are moving from AI-assisted coding to AI-orchestrated delivery. The winning pattern is not “humans out”; it is “humans at the highest-leverage layer” where architecture, decomposition, quality gates, and risk decisions happen. This post synthesizes the NotebookLM research notebook b4a11a4b-0e9f-4615-ad14-96d0c8bee177 and highlights eight shifts changing engineering strategy right now. ...

Large Language Models (LLMs) are revolutionizing how we interact with technology. But their true potential often unlocks when they break free from their digital sandbox and interact with the real world – fetching live data, triggering actions via APIs, or using specialized software tools. Enter the Model-Context Protocol (MCP) and similar frameworks, designed to be the universal adapter, the “USB-C port,” connecting these powerful models to the vast ecosystem of external tools and data sources. ...