Quick persistent AI agent harness tips (for OpenClaw vs. Hermes)

-OpenClaw, and Hermes Agent by Nous Research approach persistent AI agents from different angles. -OpenClaw centers on a gateway for sessions, tool routing, and multi-channel connections. It delivers broad ecosystem reach, multi-agent orchestration, and integration with existing chat and workspace tools. Upside: quick start and clear control. Downside: setup work and fragility when things change. -Hermes builds around the agent's own loop. It supports self-evaluation, learns from prior runs, and persists for continuous improvement. Memory is straightforward by default; repeating tasks need less hand-holding. Tradeoff: smaller community toolset and fewer integrations. -At core, it's architectural diff: one treats the agent as a job in a control system; the other makes the agent's learning and refinement central. In production or daily things, this choice yields either a solid unchanging helper or one that grows sharper on your specific tasks. Both have value. Your options are: prioritize wide connections or deeper self-building capability.