Rethinking the value of multi-agent work-flow: a strong single agent baseline

The provided text explores whether multi-agent systems (MAS) can be effectively replaced by a single agent simulating complex workflows through multi-turn conversations. Research indicates that homogeneous workflows, where multiple agents use the same base model, can be replicated by one agent with significant computational efficiency gains via KV cache reuse. The authors introduce OneFlow, an automated algorithm that utilizes dual meta-LLMs and Monte Carlo Tree Search to design streamlined, high-performance workflows specifically for single-agent execution. Experimental results across various benchmarks demonstrate that this single-agent approach matches the accuracy of multi-agent setups while reducing inference costs. However, the study acknowledges that heterogeneous workflows involving different base models still offer unique benefits that a single model cannot yet fully capture. Consequently, these findings establish the single-LLM implementation as a powerful new baseline for future multi-agent research.