SIMHEAD Blog by Peter Kim

Deconstructing the AI Stack: From Classical Logic to Agentic AI A six-layer view of the AI stack, from rule-based logic and learned patterns to generative systems and autonomous tool-using agents. Introduction Artificial intelligence is often described as if it were one giant invention: a single machine that suddenly learned to talk, draw, code, plan, and reason. That framing is convenient, but it hides the most useful truth about AI. Modern AI is not one monolithic technology. It is a layered stack. Each layer was built on earlier breakthroughs, and each layer changed what computers could do. The easiest way to understand today's AI systems is to stop asking, "What is AI?" and start asking, "Which layer of AI are we talking about?" Classical AI used explicit human rules. Machine learning shifted the work from writing rules to training algorithms on data. Neural networks introduced flexible architectures inspired by biolog...

Building the Modern AI Stack: MCP, RAG, and Agent Skills Explained Introduction Large language models are powerful, but a model by itself is still isolated. It can reason over the prompt it receives, generate text, write code, and make strong inferences, but it does not automatically know what happened in your Slack workspace this morning, what is stored in your private vector database, which files exist in your repository, or how your deployment pipeline is supposed to run. Out of the box, the model is an engine without enough context, a capable planner without live application state, and a fluent assistant without direct access to the tools required to finish real work. That gap is the core design problem behind the modern AI stack. Teams do not only need smarter language models. They need models that can safely connect to external systems, retrieve trustworthy knowledge, and execute useful actions. This is why three extension patterns have become importan...