Nous Research's HermesAgent: an AI agent that learns skills and remembers across sessions

Most AI chatbots today "lose their mind" every time you open a new conversation: everything you taught them last time vanishes completely. An open-source project from Nous Research called HermesAgent is pursuing the opposite direction — an AI agent that accumulates skills through each use and retains its memory across working sessions.

How self-learning agents differ from forgetful ones

The experience is familiar to anyone who has used a chatbot: at the start of each session you must re-explain the internal deploy process, the company's API conventions, your file-naming habits... The conversation ends, and all of that "knowledge" is wiped. For individual users it is an annoyance; for businesses it is a paradox: the more you use it, the more you have to re-teach it.

Self-improving agents target this weakness head-on. Instead of each session starting from a blank page, the agent actively records what it learned after each task — how to handle a certain type of error, a multi-step procedure, a user's convention — and reuses it next time. The agent's value therefore compounds: the more you use it, the better it understands you, instead of the more you use it, the more familiar — then forgotten — it becomes.

What HermesAgent is

HermesAgent is an open-source AI agent project pursued by Nous Research — the lab well known for the Hermes model family built on an open-weights philosophy. Two characteristics are widely shared about the project:

• Learns skills through use: after completing a task, the agent distills the approach into a reusable "skill", rather than fumbling from scratch the next time.
• Memory across sessions: memory is not confined to one chat — what the agent learned in a previous session is still there when you come back later.

Its open-source nature is the third notable point: users can read, modify, and deploy it themselves, without being wholly dependent on a closed service owned by some corporation.

How an agent "learns skills" in practice

While each project has its own implementation, this new generation of agents typically combines three mechanisms users can observe:

1. Persistent memory: notes, preferences, and work context are stored as files or a database outside the conversation and reloaded when needed.
2. Skill library: once a complex task is completed, it can be distilled into a procedure — for example, "how this team builds and publishes documentation" — that can be invoked next time with one short sentence.
3. Self-evaluation loop: the agent compares its results against the requirements, notes what worked and what didn't, and performs faster and with fewer errors next time.

The tangible result users feel: less repeated instruction, repetitive operations get automated over time, and the agent becomes a "collaborator" who grows closer to your work — instead of an assistant you have to reintroduce yourself to every day.

Why this direction matters

• Lower cost of "teaching AI": most real-world agent usage time goes into supplying context. Compounding memory cuts out that repeated cost.
• Knowledge stays with the organization: employees come and go, but the skills an agent learned from internal processes remain — a new kind of knowledge asset.
• Transparency and control: with an open-source project, a business can inspect what the agent remembers, where it is stored, and who can read it — something nearly impossible with a closed chatbot.

Things to keep in mind

The more an agent remembers, the greater the responsibility:

• Wrong memories get "learned" too: if the agent draws the wrong lesson from a botched task, it may repeat the mistake with confidence. Periodically review and clean the memory store.
• Security of stored memory: memory containing internal information must be protected like a vault of confidential documents — access control, backups, and consider self-hosting on private infrastructure.
• It is not magic: the agent still needs clear initial instruction; "self-learning" shortens the long road but does not replace defining the problem correctly.

A perspective for Vietnamese businesses

For Vietnamese SMEs, this generation of self-learning agents opens up a more realistic scenario: instead of buying seats on a closed chatbot and re-teaching it every day, they can deploy an agent on their own infrastructure and let it gradually absorb internal standard operating procedures — from customer-care workflows to data-processing pipelines — accumulating them into a digital asset of the company. Technical teams can also contribute to and verify the project directly, since it is open.

Conclusion

The AI race is shifting from "smarter models" to "agents that can learn and remember". Nous Research's HermesAgent represents that direction: skills that accumulate through use, memory that spans sessions, and everything open for anyone to verify. If you are tired of "reintroducing yourself" to your chatbot every morning, this is a technology trend worth following.

Source: compiled from technical discussions shared on Facebook, edited by Wotech Electronics.

If your business wants to explore self-hosted AI agent solutions for internal processes, the Wotech Electronics team is ready to advise and accompany you through deployment.