What I see when people try to get real work done with AI. What works, what breaks. No hype, just things that run.
An AI agent isn't magic: a model, some tools, and a loop that makes them work together. Building one once is the best way to understand it. Rebuilding everything for every need means forgetting that others have already thought about it.
Anthropic published a table of the five steps of AI adoption in July. Microsoft had published the same thing in May, with numbers. At every rung, what blocks you is never motivation - and the most useful part of the document isn't the list of rungs.
Prompt engineering, context engineering, agentic, harness, loop, graph. Nine names in six years for roughly the same thing. Who coined what, on which date, and what actually matters when you are not an engineer.
A knowledge graph is a map of everything you have - documents, PDFs, notes, screenshots - built once, with the links between them. Your AI consults the map instead of re-reading it all: cheaper, faster, and a memory that sticks.
A skill is your know-how, packaged: your AI stops being a blank chatbot and 'already knows the job'. Thousands are ready to plug in. How to use them - and why you audit before you plug in.
You describe the video in your own words, the AI writes the code, you get a real MP4. No timeline, no After Effects. Here's what it is - and what it isn't.