Hermes Agent works best as a multi-model workflow

Hermes Agent’s Mixture of Agents changes the usual setup where one AI model receives a task and produces the whole answer alone. Several models work on the same problem in parallel, then an aggregator combines their separate answers into one final response. The proposed setup connects Hermes, Fusion, Sakana Fugu, Agent OS, and shared memory so the workflow can be reused instead of rebuilt each time.

The main claim is that people do not need to wait for the next closed frontier model if they can combine useful models that are already available. One model may catch a technical risk, another may notice a weak assumption, and the final model can keep the strongest parts. Early hands-on results were mixed: an orchestration setup with Hermes, Deepseek V4, and Claude was abandoned after weak early results, though the problem may have been poor workflow wiring rather than the idea itself.

The related material makes strong claims about beating Claude, Opus, and GPT, but the supplied excerpts do not include benchmark numbers or a clear testing method.