Innovators
1 Curated Dialogues
Hermes Agent
Key Views & Dialogues
Hermes Agent: Agents that grow with you |Episode #357|
Hermes Agent turns successful tasks into reusable skills, making a restaurant booking that took 30 or 45 minutes initially instant the next day. Nous’s debugging agent accumulated skills over about a month, showing how agent infrastructure can spread expertise and potentially sell cheaper or faster outcomes, while ownership and value capture remain unsettled. NVIDIA could sponsor Western open models because workloads run on its chips, though Quesnelle’s optimism is limited to the short term.
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- Hermes Agent’s product thesis is that useful workflow knowledge accumulates with use. Jeffrey Quesnelle describes an agent that turns successful tasks into reusable skills without being explicitly told to save them. A restaurant booking took 30 or 45 minutes initially; the following day, it reused the procedure immediately—the practical meaning of “the agent ought to get better the more you use it.”
- AI value will depend on completed work, with models and harnesses contributing jointly. Quesnelle’s analogy is “the model is the brain, and the harness is your body”: stronger execution infrastructure can let a weaker model outperform a stronger one. His economic endpoint is customers paying for outcomes delivered more cheaply or quickly, while ownership and value capture remain unsettled.
- Open models need sponsors whose businesses benefit from their adoption. Quesnelle contrasts Meta’s costly model-development setbacks with Chinese companies using open releases to gain prominence. He sees NVIDIA as a potentially significant Western sponsor because workloads run on its chips: “the house always wins.” His optimism explicitly applies to the short term.
- The enterprise opportunity is spreading one engineer’s expertise across an organization. Nous’s internal debugging agent accumulated skills over about a month, allowing support staff to investigate customer problems and obtain reports and root causes. Collaborative and multi-tenant deployments are the next direction, but Quesnelle names no partners or commercial results.
- Small teams can gain leverage without reproducing a frontier lab’s budget. Nous pursued “1,000x improvements” around expensive research bottlenecks, including scarce engineering talent. Quesnelle credits a team member he names as Technium in one passage and Teknium in another, whom he describes as not previously able to write a large application from scratch, with building what he calls GitHub’s “number one open source repository.”
- Effective delegation requires explicit success criteria and careful task selection. Quesnelle recommends workloads requiring “infinite patience but very low creativity,” rather than agents modeled on executive job titles. Users should describe outcomes and evaluation standards; human taste remains a limitation he would not delegate away “at least right now.”
- The long-term risk is that automation weakens the people using it. Quesnelle worries that users could “check out of thinking,” especially over a generation raised to assume the computer knows. Nous’s stated standard is that AI should make people “better today than yesterday and better tomorrow than today”—a goal he says also requires responsible use.