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Recent research suggests that multi-agent systems are often a temporary engineering workaround for limitations in model routing, memory, and coordination rather than a final design goal. Studies from institutions like the University of British Columbia demonstrate that many complex agent swarms can be collapsed into a single model to significantly reduce costs and latency without sacrificing quality. While multiple agents remain essential for governance, heterogeneous capabilities, or physical coordination, many current structures merely serve to prevent tool confusion. Experts recommend starting with the simplest possible system and treating multi-agent setups as training scaffolds to be eventually internalized into more efficient, unified models. Furthermore, the industry is moving away from verbose natural-language handoffs between agents in favor of high-bandwidth latent communication and structured state transfers. Ultimately, the goal is to shift from performing theatrical "personas" toward managing precise skills under strict computational budgets. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit lambpetros.substack.com (https://lambpetros.substack.com?utm_m...)