This is a submission for the Hermes Agent Challenge
Most AI Agents Are Forgetful. Hermes Agent Is Betting That Memory Matters More Than Intelligence.
Every few weeks, a new AI agent framework appears. For about ten minutes, it feels revolutionary.
Then reality kicks in.
The agent forgets everything. Fancy Reasoning over lacklustre memory.
Hermes Agent is interesting because it treats intelligence as something that compounds. This changes everything.
That is the insight I would carry forward from it.
Hermes is not only about planning or tool use or multi-step reasoning, although it does those things. It is about turning repeated work into remembered work, and turning remembered work into better future work.
If the next generation of agent systems is going to matter, it will not be because they can produce a good one-off answer.
It will be because they can learn the shape of the work, keep that knowledge, and become more useful every time they are used.
Hermes is pushing in that direction.
IMO, that is why it is worth building on.
TL;DR Hermes is one of the few agent systems explicitly designed around the idea that accumulated knowledge should survive beyond a single session.
Top comments (2)
Interesting thesis, and I half-agree. Memory absolutely matters - a model that can't recall your context is doomed to be a clever goldfish. But I'd reframe it: it's not memory OVER reasoning, it's that memory unlocks the VALUE of reasoning. Great reasoning on a blank slate every time is wasted; the same reasoning applied to accumulated context compounds. They're multiplicative, not competing.
The practical catch (and the reason I'd hesitate to declare memory the winner) is that memory introduces its own hard problems: relevance (retrieve the right thing), staleness (forget the wrong thing), and cost (every remembered token is a token you pay to re-process). A system that "remembers everything" often reasons worse because the signal drowns in remembered noise. The discipline is curated memory + sharp reasoning, not maximal memory. That balance is exactly what I tune in Moonshift (a multi-agent pipeline that ships a prompt to a deployed SaaS) - scoped, relevant context per agent rather than a giant memory dump, which keeps both quality and cost (~$3 flat) in check. Thought-provoking post. Do you see memory replacing reasoning effort, or amplifying it? I lean amplify - the best systems will need both, well-managed.
Provocative and I mostly agree. Marginal reasoning gains are flattening while memory is still wide open, and most "the AI is dumb" moments are really "the AI forgot," not a reasoning failure. An agent with mediocre reasoning but perfect recall of context, prior decisions, and what was already tried beats a genius with amnesia on real multi-step work. I'd frame it as both-and though: memory makes reasoning usable by feeding it the right inputs. The harness that manages memory is doing more of the work than people credit. That's most of what I build in Moonshift. Where do you draw it, is it memory specifically or context-management broadly that's the real bottleneck?