For most tasks, I agree. One agent with a good harness wins. The case for multiple agents is when the context required to solve the problem exceeds what one agent can hold. This Putnam problem needed more working context than fits in a single window. Decomposing into subgoals lets each agent work with a focused context instead of one agent suffocating on state. Ideally, multi-agent approaches shouldn't add more overall complexity, but there needs to be better tooling for observation etc, as you describe.