Scaling smart: How LLM developers are solving multi-agent orchestration bottlenecks
AI models are hitting a bottleneck, where raw parameter scaling to improve their performance no longer cuts it. Performance improvements per doubling of AI model parameters are gradually diminishing, leaving multi-agent coordination as the next option. Multi-agent coordination implies two or more AI models working together and sharing data to handle workflows. Instead of relying on one model to handle everything, tasks are divided between different agents, e.g., one writing code and another validating the code.
Coordination involves breaking AI tasks into smaller pieces, assigning each piece to the best-suited agent, and controlling how the agents interact with each other to achieve a common goal. However, this coordination is facing its own bottlenecks. Moving from one AI agent to two, three, or five multiplies potential state synchronization errors and causes token bloat and high latency. Error loops can consume unnecessary tokens, and hallucinations can pass from one agent to another,...
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