Quick intro to Docker, Kubernetes and latest network tech

Over the past two months, we have systematically constructed a complete mental model of modern AI architectures. We laid the overarching blueprint in Deconstructing AI, zoomed into the matrix-math of the parameter file in Deconstructing the Brain, and animated that static intelligence in The Agentic Control Loop. From there, we scaled up, connecting isolated agents into collaborative swarms via the A2A Protocol, and finally gave them hands to interact with enterprise infrastr

In my last piece, Deconstructing AI, I explored how a modern autonomous AI network functions. I mapped out how an Orchestrator Agent takes a natural language request, delegates tasks using the A2A protocol, and interfaces with the physical world via MCP servers. But at the absolute center of that entire architecture sits the LLM (Large Language Model)—the reasoning core giving the instructions. This week, I am zooming in to this. I want to crack open that "brain" to look at i

When you type a complex request into a modern AI system, it doesn’t just generate text—it orchestrates a symphony of specialized tools, protocols, and APIs to get the job done. To understand how Large Language Models (LLMs), Agents, the A2A Protocol, and MCP Servers work together, we will follow a single, practical use case from start to finish. Overview The diagram below maps out the exact lifecycle of a user request as it traverses the different layers of a modern autonomou