Does multi-agent AI pay off, and can you automatically choose the right pattern for the task?

▤ Summary

The article discusses whether multi-agent approaches are worth using and how to choose the right pattern for a task. It presents FEDOT.MAS as a framework for building agent-based workflows and compares several orchestration patterns such as single, chain, voting, eval_optimizer, orchestrator, and blackboard. The piece includes code examples and benchmark results on LLM tasks like GSM8K, MMLU, and LogiQA, showing that different patterns can improve performance depending on the model and task. It also highlights that automated pattern selection can outperform a one-size-fits-all setup and may improve results on harder reasoning benchmarks.

▦ Linked Companies