Hermes Agent: Agents that grow with you

What It Covers

Nous Research cofounder Jeffrey Cannell explains how Hermes Agent, now the number one open source repository on GitHub, was built internally as a model research tool before being released publicly, and how its self-improving skill and memory systems differentiate it from competing agent harnesses in the market.

Key Questions Answered

• •Agent Skill Accumulation: Hermes Agent automatically creates reusable skills without explicit programming. When the agent solves a novel problem — such as navigating bot-detection systems to book a restaurant reservation in 45 minutes — it logs the method as a named skill. Subsequent identical tasks complete instantly by retrieving that stored skill, compounding efficiency the longer the agent runs.
• •Outcome-Oriented Prompting: Users should describe desired end states and explicit evaluation criteria rather than step-by-step instructions. Unstated assumptions about quality — tone, format, aesthetics — will never be fulfilled because the model defaults to statistically average outputs. Writing out every condition you would use to judge success gives the agent a concrete target to optimize toward.
• •Agent Deployment Targeting: Identify workflows requiring infinite patience but minimal creativity as the highest-value automation targets. Tasks a non-specialist human could theoretically complete but would never willingly sustain — such as reading thousands of server logs to root-cause errors — are ideal. Avoid one-to-one role mapping like "CEO agent," focusing instead on specific operational processes.
• •Organizational Skill Compounding: A single engineer using Hermes Agent to debug infrastructure problems passively builds a shared organizational knowledge base. After one month of use, Nous Research's support staff could independently query complex backend systems without engineering involvement, because the agent's accumulated skills became accessible across the entire team without additional training or coding.
• •Western Open Source Shift: NVIDIA's commitment of approximately 20 billion dollars toward training Western open source models over several years represents a structural change in who funds open AI development. Because all AI workloads ultimately run on NVIDIA hardware, the company has direct business alignment with expanding open source model usage — unlike Meta, whose Llama program lacked equivalent commercial incentive.