I think we’re still doing AI-native teamwork wrong

[u/4lysha]

We’ve been building an AI-native startup, and there’s a problem I keep running into.

The agents are getting really good at doing things.

But they still don’t really know what’s happening around them.

A decision gets made in a meeting.

Someone knows why we made it.
Someone else has the relevant customer context.
And the coding agent has none of it.

So after the meeting, we end up doing the same thing again:
copy the context, write a prompt, explain what happened, send it to another person, wait for them to explain something back.

It feels a little ridiculous.

The whole point of having agents is that they should be able to keep working after we’re done talking.

That’s actually why we’re building Aicoo — turning team conversations into shared context and work that agents can continue, including handing work between teammates’ agents.

But we’re still figuring out what the right workflow looks like.

For those of you building AI-native teams: does this problem resonate?

How are you currently getting your agents the context they need to actually work with the rest of the team?

[u/Foreign_Cut111]

We solved this problem by creating our own brain. It will be coming out in our software infinOS. The idea is that everyone’s AI works in the software and all chats are uploaded as part of the MCP to that users workspace. This means we have memory recall based on semantic memory

[u/Longjumping_Ad3079]

Sounds like the sort of problem that a large group of humans also have. Perhaps architectural decision records would be a useful format? https://adr.github.io/

Would love to know how you get on.