The LLM provides intelligence (the "brain"), but the agentic harness provides the ability to interact with and affect the real world (the "body"). A less intelligent model with a capable harness can outperform a smarter model with a limited one, shifting value to the application layer.
AI lacks shared human context, history, and aesthetic judgment. To get desired outcomes, you must be hyper-explicit, articulating all unstated assumptions and evaluation criteria that you would normally take for granted when communicating with another person.
AI models, trained on data divorced from our lived, biological experience, lack the innate aesthetic sense that almost all humans possess. This makes taste and aesthetic judgment a uniquely human and valuable contribution as AI handles more logical and computational tasks.
Hermes Agent began as an internal tool to automate AI research and overcome talent scarcity. Releasing it to the public resulted in a "floodgate of product market fit," demonstrating the power of building tools to solve your own company's problems first.
The original architect of Hermes Agent, Technium, was able to build a world-class application without deep coding expertise by leveraging modern AI tools. This demonstrates that vision and drive can be more critical than traditional programming skills in the current AI landscape.
The ideal tasks for agents are those a human could theoretically do but would never have the patience for, like reading every single log file. Don't try to automate creativity; instead, focus on high-volume, repetitive, or tedious processes that are currently bottlenecks.
After Western interest in funding large open-source models waned due to high costs, Chinese companies adopted the strategy. They used open-source releases to quickly elevate their company profiles and establish themselves as top-tier players on the global stage.
Instead of pre-programming specific functions, Hermes Agent is designed to observe user interactions, identify important achievements, and autonomously create new "skills" for future use. This allows it to adapt and improve organically, breaking from traditional software design paradigms.
Unlike other tech giants, NVIDIA's funding of open-source models directly drives its primary revenue source. Every successful open-source model, regardless of who trains or uses it, ultimately runs on NVIDIA hardware, making them the "house" that always wins.