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.

I also never understood this desire to build an agent org chart with persistent identities, often giving them names. Paperclip seems to advertise that model. I think it's much simpler and more flexible to simply spawn an agent for each task.

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.

If you leave agents to figure it out, they will spend time figuring it out. If you give agents run-books and roles, they will align with the role at once.

See Conways law for how to mitigate this

Are you making a subtle reference to the Bobiverse series?