Memory should be managed by an agent with its own meta-memory — a system that learns which information sources matter and which to ignore, rather than relying on static RAG pipelines.
Lindy's memory agent runs every ~15 minutes (not 24 hours), maintains public team and private personal memory layers, and crucially has its own meta-memory — a prompt file that teaches it what to remember and ignore. This agent learns to skip noise channels like automated logs, effectively training itself on organizational context the way a model trains on data. ✦ AI generated
Flo Crivello · The Cognitive Revolution · 2026-08-10 · original ↗
starts at this moment · 11:28
“How do you deal with idiosyncratic special case things that could overwhelm or mislead the system?”
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 on 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 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 and smarter about what actually matters... 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 I'm not going to keep spending time on those channels. There's nothing for me to learn there.
verbatim transcript · starts at 11:28
11:28think 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 on 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 and understands what it is that
11:53it's looking at and by virtue of accumulating that knowledge little by little about the organization gets smarter and smarter about what actually matters or it's actually sort of similar to training a model 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
12:15data 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. So it's
12:36a 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 I'm not
12:57going to keep spending time on those channels. There's nothing for me to learn there. Thing that's been critical for us has been building meetings as as a first class citizen in this system. Like I really do believe that meetings are very underrated as a source of information. they they where like 90% of of the most up-to-date data leaves about the company like everything that matters inside the company has a meeting around
13:22it every relationship every project every initiative everything has a meeting around it and so I think like those multiplayer agentic systems cannot really get meetings to like granola or whatever like I think you have to really incorporate it in your system as a first class citizen and so what we did is that you know we built that first class citizen we have meetings now as a first
- ·Memory maintained by agent, not pipeline
- ·Agent has own meta-memory for context
- ·Learns what matters vs. noise
- ·Gets smarter over time about priorities
- ·Runs every ~15 min, not daily
- ·Two layers: public team + private personal
- ·Prompt file teaches what to remember
- ·Auto-skips irrelevant channels (e.g. logs)
- ·Agent discovers noise channels on first crawl
- ·Learns to ignore automated logs
- ·Improves judgment incrementally
- ·Mirrors how models train on data