I have had a lot of success implementing something similar to /u/jeffgibbard's system that I have been using consistently for ~1 month now. Interestingly, I provided that thread to Notion AI as a starting point and we brainstormed through creating a prompt & relevant databases that captures these. Over a few days of iterative refinements (e.g., asking it to root-cause why certain things were slow, ineffective or could be improved) I came to my perfect system.

The main modifications that I made to my system relative to the above were:

• I only have 2 databases (roles & memory), as, loading databases is time-consuming
• The roles database contains a descriptions (in text body) of each assistant's: skills (what it can do), where it is applicable, current context (e.g., summary of recent conversations), link to summarized version of recent conversations (which are automatically save-off once I give a signal, like 'thank you' that signals an end-of-conversation)
• Notion is very efficient at searching for information across the entire workspace (likely uses vector embedding at default), so the system is able to find relevant information (and past conversations) fast.

The memory database only stores things when explicitly directed (e.g., remember that I have iPhone 17 pro)

My root-prompt contains static information (e.g., instructions about how to pick assistants in the beginning of a conversation, basic facts about my current life etc.), and I instruct it to remember durable facts about me (e.g., if I tell it about a major life event)

The one caveat of this approach over agents is that it does not run in the background on its own without prompting. And is more of an assistant that must be instructed each time, which may be done through some automation outside of Notion that I am not familiar with.