alt Hacker NewsI'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.
Replies
> You now have to account for the unknown cognitive construct of a completely different intelligence.
But your LLM training corpus covered that, right? /i
> 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 I am trying to solve and I have what I call smart repositories that demonstrates this at
https://github.com/gitsense/smart-ripgrep
https://github.com/gitsense/smart-codex
The issue I am finding is, getting the agent to pull what it needs, even when the data is there is still challenging since LLMs are trained on blind discovery where the pattern is:
grep -> read -> grep -> read ...
What is working for me now is thanks to Pi (pi.dev). I am working on a pi-brains extension that makes it dead simple to control the lifecycle for an agent so if I detect that it uses `rg` without `gsc rg`, I can block the agent and inject a steering message that says always search with context.
I can also see if they try to "read" without first looking at the files metadata and so forth.
I'm finalizing things right now, but I think pi with my brains extension should allow domain experts to better guide agents so they can find what they need, when they need it.