The memory agent learns which Slack channels to ignore

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 11:06

   slack channels at my company waymark we've had various you know logs bumped into slack over and over and over again how do you do how do you identify these idiosyncratic special case things that could overwhelm or flood or mislead them get to the the real good stuff so that's the next sentence question the i think the the main way that we've solved this is the fact that the memory is maintained by an agent itself i think this is why i'm ultimately quite bearish on rag as an approach and I'm very bullish on this like agentic management approach because you have an actual agent which has its own memory so you have a sort of meta memory and and and understands what it is that it's looking at and by virtue of accumulating that knowledge little by little about the organization gets smarter smarter about what actually matters or it's actually sort of similar to training a model

2. 12:04

   because like you know if you were to insert poisoned data into the model data set like hey you know like i don't know darth vader was a woman you know like it would get the data but it would be drowned by all the correct data you know so here it's the same it's like if you have enough memories and if you feed that to a system that understands it if the system ends up understanding And so the very concrete example you are using is actually an emergent behavior we have seen the memory agent adopt because the memory agent has its own memory.

3. 12:35

   So it's a sort of meta memory about how do I manage my memory? What sources of information matter? Like, you know, all of that. And which ones are trustworthy and all of that. And we have noticed that the first time the memory agent crawls the Slack, it finds some of those channels, like many organizations have them, like those like log channels. And it learns to ignore them. It's like, I'm not going to keep spending time on those channels.

4. 12:58

   There's nothing for me to learn there. The thing that's been critical for us has been building meetings as a first-class citizen in this system. Like, I really do believe that meetings are very underrated as a source of information. They all wear, like, 90% of the most up-to-date data leaves about the company. Like, everything that matters inside the company has a meeting around it. Every relationship, every project, every initiative, everything has a meeting around it.

5. 13:26

   And so I think those multiplayer systems cannot really get meetings like Granola or whatever. I think you have to really incorporate it in your system as a first-class citizen. And so what we did is that we built that first-class citizen. We have meetings. Now it's a first-class citizen in Lindy. And most importantly, it's not just a Granola. Oh, it's recording your meetings. and you can do all of that stuff.

6. 13:54

   But it goes beyond that and it feeds these meetings to your memory agent. And now, if a meeting was public, so you can set up meeting folders which automatically add meetings instead of meeting folders which automatically add meetings to themselves and share them with your entire team. And so then it summarizes all of the meetings that are in these folders. And again, if a meeting was added to a public meeting folder that shared with the entire company then it it debates the team's memory with it and so it keeps updating that context on an ongoing basis and now you can like chat with it's basically like a image that's been in every meeting in the company you can chat like that entire corpus of meetings you can be like what are customers saying like what's the biggest request lately like you know what's been the feedback about this and that teacher and so