One signal

Hermes Agent’s much-discussed “learning loop” works through saved memories and reusable skills, not by retraining the underlying AI model, according to an explainer published by OrcaRouter on 23 October. The distinction matters: the agent’s accumulated procedures are files that can be inspected and changed.

The explainer says Hermes can create skills after complex tasks, revise them through later use and retrieve earlier sessions. That offers a practical picture of what “learning” means here, rather than a claim that the model itself gets smarter. OrcaRouter also notes that the project has not published a benchmark showing the loop makes the agent perform better. A useful mechanism, then, but the improvement claim still needs a test.

Discuss: Would you trust an agent more if you could inspect and edit what it learns, or do you want benchmark results before calling that learning?

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