How AI Agents Work
The foundational model — what LLMs actually are, how tools extend them, and how the request/execute/return loop powers every AI agent you'll ever build or use.
Lessons
Architecture Overview — For New Developers
The big picture of how AI agents work — three simple building blocks that turn a text predictor into something that can actually do things.
Architecture Overview — For SRE / DevOps
The runtime architecture of an AI agent — process model, trust boundaries, transport layers, and where each component fits in your infra.
Architecture Overview — For Tech Leaders
The three-layer model that powers every AI agent — and the architectural decisions each layer puts on your plate.
Architecture Overview — For Experienced Developers
How tools, skills, and MCP stack together — the three extensibility primitives, the host routing layer, and the mental model that ties them all.
What is an LLM — For Experienced Developers
The transformer architecture, tokenization mechanics, and inference pipeline — everything you need to understand how LLMs actually work under the hood.
What is an LLM — For New Developers
A plain-language introduction to Large Language Models — what they are, how they work at a high level, and why they matter for the code you'll write.
What is an LLM — For SRE / DevOps
LLMs from an infrastructure perspective — resource requirements, inference costs, latency characteristics, and what you need to know to run them reliably.
What is an LLM — For Tech Leaders
The accurate mental model for Large Language Models — what they actually are, what they can and can't do, and what that means for your technical strategy.
Tools — For New Developers
How AI models interact with the real world — the request/execute/return loop explained from scratch with simple examples.
Tools — For Tech Leaders
What tools mean for AI architecture — the trust model, the control boundary, and what it means that the model never executes anything directly.
Tools: A Deep Dive
What tools actually are, how the request/execute/return loop works, parallel calls, error handling, and how to write tool definitions that the model uses correctly.
MCP — For New Developers
How AI agents connect to the outside world — MCP explained from scratch with plain-language examples and diagrams.
MCP — For Tech Leaders
Why MCP matters for your AI strategy — the standard protocol that eliminates vendor lock-in and turns N×M integration problems into N+M.
MCP: Model Context Protocol
The open protocol that standardizes how AI agents connect to external systems. JSON-RPC internals, transports, the three primitives, and how to build a custom server.
Skills — For Experienced Developers
Just-in-time retrieval-augmented prompting — how skill files work, how to structure them, and how they compare to RAG and fine-tuning.
Skills — For New Developers
What skill files are, why they exist, and how to create your own — giving AI agents expert knowledge exactly when they need it.
Skills — For SRE / DevOps
Skills from an infrastructure perspective — file layout, context budget, performance implications, and managing skill files across teams.
Skills — For Tech Leaders
How skills keep AI agents lean and expert at the same time — just-in-time knowledge loading as a context management strategy.