One argument for the persistent identity model is that you can give them special skills and context. I don't believe that's a good argument. In my experience, you can just give each agent access to the full set of tools (including RAG search) in the form of documentation and skills, and they'll use that efficiently. Skills have a tiny front matter and hardly bloat the context, and the documentation just needs to be tree-structured with cross-links for progressive disclosure. LLMs navigate that efficiently, so the cost of having all context accessible is low, while the risk of cutting the context boundaries for the persistent identities non-optimal for a given task is rather high IMO.
Efficiency wise I'd imagine at least picking a leader from the start just saves initial hustle and context window
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.
On what basis do you come to this conclusion with certainty?
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.
I don't think you can reduce context enough on a local LLM to get performance comparable to a 500k context without those reductions...