c3z_

my 3 cents as I'm working on agentic CRM

add rac steward to check and validate "quality" of ADR (freshness, completness, clarity, brewity, etc.)

so expect your tool to help clean it's own database ;)

great approach with dogfooding!

the failure I hit most is not that the agent forgets, it is that it re-opens things the team already closed. So treating ruled-out paths as first-class is the right instinct

quick question why mcp instead of cli tool? I'm mostly building the latter (less context burden), but help me understand your own ADR regarding this

why not both?

also after quick review on your doc I don't see where is ADR portal for humans? some plans for UX or this is inside git repo?

starred and followed

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.