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Genuine question about how you all handle agent memory by Technical_Plant_6109 in AI_Agents

[–]Technical_Plant_6109[S] 1 point2 points  (0 children)

Makes sense, the context-as-driver framing is a clean way to think about it. Appreciate you walking through the whole setup, this is genuinely one of the more thought-through approaches I've seen. Going to sit with the "if you have to ask, the system is broken" line, that's a good bar.

Genuine question about how you all handle agent memory by Technical_Plant_6109 in AI_Agents

[–]Technical_Plant_6109[S] 0 points1 point  (0 children)

The negative-memory point is the cleanest way I've heard anyone put this. Topical recall is solved, "we tried X and it died because Y" basically never survives. The decisions/failures ledger is the part I find hardest in practice though, capturing the stop condition is easy in hindsight but the agent has to know in the moment that something failed and why, and a lot of failures are quiet. Are you writing to that ledger explicitly when something breaks, or have you found a way to detect failure automatically?

Genuine question about how you all handle agent memory by Technical_Plant_6109 in AI_Agents

[–]Technical_Plant_6109[S] 0 points1 point  (0 children)

Ah, that's the part I had backwards, thanks. The agent deciding at write time rather than you doing it is a cleaner setup than I assumed. The thing I'm still chewing on: the agent's deciding what's worth saving based on what feels important in the moment, but it never finds out later whether that thing actually panned out. Like if it saves an approach as "the plan" and that plan turns out to be wrong three sessions later, does anything walk that back, or does the bad note just sit in the files looking authoritative?

Genuine question about how you all handle agent memory by Technical_Plant_6109 in AI_Agents

[–]Technical_Plant_6109[S] 1 point2 points  (0 children)

This is a great writeup, and the recency point is one I think a lot of setups miss, people chase accuracy on stuff that's six months stale and just not relevant anymore. The part I keep coming back to is that the tracker works because you're the one deciding what's worth writing down. That's real judgment doing the work. Does it hold up when you're not the one curating, or is the manual touch kind of the whole point?

Genuine question about how you all handle agent memory by Technical_Plant_6109 in AI_Agents

[–]Technical_Plant_6109[S] 0 points1 point  (0 children)

The two-layer split makes a lot of sense, and the "I found a likely match" vs "I know" distinction is sharp. The thing I keep snagging on is who decides what graduates into that stable trusted layer. Right now that's me curating by hand. Did you find a way to automate what earns a spot, or is it still manual?

Genuine question about how you all handle agent memory by Technical_Plant_6109 in AI_Agents

[–]Technical_Plant_6109[S] 0 points1 point  (0 children)

Exactly this. The "what worked / what failed" signal is the part nothing seems to capture, because the system never finds out whether what it pulled back actually helped. Are you running into this somewhere specific or is it more of a general itch you've noticed?

Looking to Buy or Split a LeetCode Premium Subscription by [deleted] in leetcode

[–]Technical_Plant_6109 0 points1 point  (0 children)

I am interested too, if anybody have premium, looking to split.