Autonomous multi-agent systems are like self-driving cars: proof of concepts are simple, but the last 5% of reliability is as hard as the first 95%.
Without comprehensive and precise guidance, agents can misinterpret tasks or generate incorrect outputs. Poorly defined workflows can lead to inefficiencies or outright failures.
The complexity of multi-agent systems grows exponentially as you add more agents. Success requires addressing communication, orchestration, and memory issues.
To enhance reliability, it's crucial to ensure instructions align with the capabilities of the large language model and to define clear stopping criteria.
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