Comment by frankacter
11 hours ago
If agents are differentiated solely by system instructions (persona and focus) while sharing identical models / similar model capabilities and contexts, I agree the architectural value is minimal.
However, a multi-agent system (MAS) utilizing true separation of concerns, where each agent is equipped with highly scoped tool access, isolated knowledge bases (RAG), and defined operational constraints, significantly optimizes accuracy, latency, and token consumption.
In the referenced example, the agents share identical capabilities, toolsets, and context windows. As a result, there is no functional role differentiation, rendering the multi-agent architecture redundant.
> significantly optimizes accuracy, latency, and token consumption.
On what basis do you come to this conclusion with certainty?
Anyone who has used local LLMs will come to this conclusion.
Too much context confuses the LLM. Reduce unnecessary context and performance improves. Not to mention, too much context and you just run out of vram.
Such limitations may be mitigated in future architectures, but it certainly isn't something you can ignore currently.
> Too much context confuses the LLM. Reduce unnecessary context and performance improves. Not to mention, too much context and you just run out of vram.
I don't think you can reduce context enough on a local LLM to get performance comparable to a 500k context without those reductions...
it seems to me that would only be true if the amount of knowledge needed to complete a task far exceeds the context limit. Why would that be the case? I find most people are using horribly inefficient contexts, and a naive approach to dark factories/software eng swarms seems to only compound this
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