I've learned that for both humans and models: system > willpower. The key is entirely in designing the environment.

For me personally, that means setting up 'attention getters' for the important things in life - 'totems' that force a context switch. For AI agents, it means well-designed CLI tools that help the agent orient itself in a task and pull exactly the 'context-for-the-job' it needs right then.

This is exactly what makes building modern GenAI decision-support systems so difficult. It's no longer just about finding the right software abstractions. You now have to account for the unknown cognitive construct of a completely different intelligence.

This rings very true to me, and it's why I've been mildly obsessed for a decade plus with how to share mental models between people, and now LLMs, of any domain, be it technical, commercial, scientific or anything else. My inspiration was a book called Learning How To Learn by Novak, which TBH is so dry I'm not sure anyone I've recommended it to has actually finished it :) So then I point them to a talk here: https://www.infoq.com/presentations/concept-map/ and an app to help render the shared mental model in plain text accessible to the LLM while providing visual interactivity to the humans here: https://thinkingtools.software/concepticon/

It is interesting to compare this to LLMs - they also have the bounded context that you can see as the analogue to our working memory. It can contain enormously more bits of information than the 4 things the article says is the capacity of our working memory - but the 4 things can probably be much more complex internally - they are more like 4 pointers probably.

But at some level context engineering is very similar to what this article talks about.

Reminds me of Rich Hickey's "Simple Made Easy" talk

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