7 minutesMemory for agents (conceptual video)
Separate short-term and long-term memory decisions and decide when agent memory should be written.
LangChain. Lance Martin's framework — write, select, compress, isolate — with concrete examples of when to summarise an action history, when to offload state to files, and when to spin up sub-agents purely to protect the parent's context. Maps almost directly onto the article's section on managing 1M-token windows in practice.
Model names, pricing and capabilities change quickly. Use this for the decision pattern, then verify current model behavior before adopting it.
Apply write, select, compress and isolate patterns to manage agent context deliberately.
Experience building or operating agents with long-running context.
Last reviewed: May 18, 2026
Continue through the same learning path with the next curated companion videos.
7 minutesSeparate short-term and long-term memory decisions and decide when agent memory should be written.
5 minutesUnderstand why screenshot-based computer use is powerful, slow, expensive and brittle compared with API-native automation.
13 minutesModel prompt injection as untrusted-data mixing and design boundaries around tool use.
Hand-picked external courses that go deeper on this topic.
Isa Fulford · Andrew Ng
Ninety minutes to a year's worth of intuition. If you've started writing code that calls an LLM — or you're about to — this is the most efficient course online for closing the gap between "playing with ChatGPT" and "shipping a feature that calls an LLM."
Microsoft Learn
Prompt engineering, but for the tool most office workers will actually touch first. Microsoft's own four-part prompting framework (goal, context, source, expectation) is a genuinely useful mental model, and unlike the generic ChatGPT prompting courses already in our catalog, this one is grounded entirely in Microsoft 365 Copilot's specific quirks and grounding behavior.