Hermes Agent: the AI agent that learns with you

There is a big difference between chatting with AI and putting AI to work.

In a chat, you ask for an answer. At work, you expect the agent to understand the goal, check sources, edit files, run commands, remember preferences, and come back with something you can verify.

Hermes Agent is an open-source AI agent from Nous Research that runs in the terminal, messaging apps, and remote environments, using tools, memory, and skills to execute multi-step tasks.

It is not just another chat window.

It is closer to a digital operator: you give it an intent, it uses tools, learns procedures, and can keep working where you actually are, including Telegram.

What is Hermes Agent?

Hermes Agent is an autonomous agent framework created by Nous Research to work with language models, external tools, persistent memory, and automations. According to the official documentation, it was designed as an agent that improves with use, creating and updating skills from experience.

The central idea is simple: instead of treating AI as an isolated text box, Hermes gives it an execution environment.

That changes the kind of task worth asking for.

Regular chat	Hermes Agent
Answers questions	Executes workflows with tools
Forgets between sessions, except for product-level memory	Can use configured persistent memory
Depends on what you paste into the conversation	Can read files, search, run commands, and check sources
Lives in one interface	Can run in the terminal, Telegram, Discord, Slack, and other channels
Delivers text	Can deliver files, commits, reports, images, audio, or automations

The boundary is not “better answers”.

The boundary is getting more of the work done without forcing you to copy and paste context all day.

How does Hermes Agent work in practice?

Hermes works as a layer between the AI model and the environment where the work happens. You choose a model provider, configure tools, and talk to the agent. When a task requires action, it calls specific tools.

In practice, it can combine five pieces:

Piece	What it does	Practical example
AI model	Reason and write	Plan an article, review code, or summarize a meeting
Tools	Act in the real world	Search the web, read files, run tests, generate images
Memory	Keep stable preferences and context	Remember response style, projects, and conventions
Skills	Reuse procedures	Follow a specific workflow for creating blog posts
Gateway	Talk through other channels	Use the agent on Telegram while it works on a server

A simple example: you ask it to “write a blog article”. A regular chatbot will probably return text. A well-configured Hermes can load the right skill, research sources, create files in the repository, generate a cover image, run npm test, commit the work, and deliver a preview URL.

That is the point.

The agent does not need to stop at the answer when the task requires execution.

Why do memory and skills change the game?

Memory and skills solve two different problems.

Memory stores compact facts that remain useful across conversations: user preferences, project conventions, stable environment details. The documentation describes memory as two main files, MEMORY.md and USER.md, injected into context at the start of a session.

Skills store procedures. They are on-demand documents that teach the agent how to execute a type of task with a repeatable standard. The skills documentation calls this progressive disclosure: the agent sees the list of skills and only loads the full content when needed.

The difference matters:

If it is...	It belongs in...	Example
Stable preference	Memory	“Gerson prefers direct replies, without hype”
Project convention	Memory	“The blog uses frontmatter with draft: true”
Reusable step-by-step process	Skill	“How to write, validate, and publish a blog post”
One-time history	Session, not memory	“Article X was created today”

Without skills, the agent has to relearn the process on every request.

With skills, it works more like someone who already knows the routine.

What does Hermes do that a regular copilot does not?

The difference shows up when the work crosses several tools.

A copilot tied to an editor helps inside code. A chatbot helps inside the conversation. Hermes tries to occupy the space between the two: conversation, terminal, files, browser, scheduling, messaging channels, and automations.

Think about three scenarios.

1. Content with real validation

You can ask for a draft article. The agent can research, write, save it in the repository, generate the cover image, run tests, and show what is ready.

The advantage is not “prettier writing”.

It is reducing the distance between idea and reviewable artifact.

2. Operational routines

Hermes cron lets you schedule recurring tasks. According to the documentation, jobs can run in fresh sessions, load skills, deliver results back to the origin channel, and even operate in no-agent mode when a script is enough.

That works for tasks such as:

• monitoring a source and sending a summary;
• checking service status;
• generating a daily briefing;
• sending a contextual reminder;
• running a script and only alerting when something changes.

3. Work through messaging

The Hermes gateway connects the agent to platforms such as Telegram, Discord, Slack, WhatsApp, Signal, and Email. For anyone away from the terminal during part of the day, that matters.

You do not need to open SSH for a simple task.

You can send a message, the agent works in the configured environment, and it comes back with the result.

When does it make sense to use Hermes Agent?

Hermes makes the most sense when the task has context, repetition, or multiple steps. If you only want a quick answer, a regular chat is enough. If you want an operator that follows procedures, uses tools, and learns patterns, Hermes starts to become interesting.

Use Hermes when...	You may not need it when...
The task involves files, commands, or validation	You only want a short explanation
The workflow repeats every week	It is a single simple question
You want to use the agent through Telegram or another channel	You always work in the same web chat
There is a clear procedure to follow	You do not know the process yet
You want automations with delivery	You only need brainstorming

The best use is not asking it to “do everything”.

It is turning well-defined routines into skills and letting the agent execute them with verification.

What should you be careful about before adopting it?

Hermes is powerful because it can use tools. That requires care.

The first concern is access. An agent with terminal and file access should not run with permissions you do not understand. The documentation recommends configuring tools, approvals, gateway access, and user allowlists carefully.

The second concern is validation. A good agent is not the one that claims it finished. It is the one that shows evidence: a passing test, a created file, a diff, a URL, a log, a job status.

The third concern is scope. If you do not define the procedure, the agent may take a plausible path, but not necessarily the best one for your business.

In other words: Hermes does not remove process.

It rewards well-defined process.

A practical example: Gerson's blog

In my case, a good use of Hermes is turning blog operations into a repeatable workflow.

A skill can define exactly how an article should be created:

1. research the topic;
2. write in Portuguese;
3. translate to English;
4. create the cover image;
5. save files in the right directories;
6. validate the frontmatter schema;
7. run npm test;
8. open a branch;
9. commit the work;
10. deliver a preview for review.

Notice the detail: the skill is not just “write well”.

It captures the operational contract of the project. That reduces basic mistakes, such as forgetting the English pair, missing the cover image, or publishing before approval.

For teams working with Power BI, data, and automation, the logic is the same. If there is a right way to build, validate, and deliver something, that way can become a skill.

How do you start without overcomplicating it?

The safest path is to start small.

First, install Hermes and configure the model. The documentation recommends hermes setup or hermes setup --portal as the most direct path. Then enable only the tools you actually plan to use. After that, create a skill for one small routine.

A good first use case is not “manage my company”.

It is something like:

• review a file and point out inconsistencies;
• create drafts from a template;
• generate a weekly report;
• validate a folder structure;
• summarize a page and save it as markdown;
• run tests and explain failures.

Once that workflow becomes reliable, you can expand the scope.

Frequently asked questions

Does Hermes Agent replace ChatGPT, Claude, or Gemini?

Not exactly. Hermes uses models as part of the system, but the focus is different: giving the agent tools, memory, skills, and execution channels. The model reasons and writes; Hermes organizes the environment so it can act.

Do I need to know how to code to use Hermes?

Not to talk to it, but technical knowledge helps with configuration and safe use. If you plan to use terminal, GitHub, servers, cron, and MCP, you need to understand permissions, files, and basic validation.

Is Hermes Agent only for programming?

No. It is very useful for development, but it also works for research, content, operations, data analysis, automations, reports, and messaging workflows. Its usefulness grows when there is a repeatable process.

What is the biggest risk of using an agent like this?

The biggest risk is giving it too much power without validation. An agent with tool access needs scope, approval for sensitive actions, and result checks. The practical rule is simple: ask for evidence, not just an answer.

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If you want to bring this kind of automation into content, data, or internal routines, check the in-company training and individual mentoring.