9 min read
Most AI products are useful in the moment.
You ask a question. You get an answer. You close the tab.
The next day, you come back and explain the same things again. You paste the same company background. You remind the AI who Alex is. You repeat that you want the recommendation first and the supporting detail second. You correct the same format you corrected last week.
The model may be smart, but the relationship does not compound.
Hermes Agent is built around a different idea: useful work should leave something behind.
When Hermes learns a durable fact about you, it can remember it. When it needs to find an older conversation, it can search its session history. When it figures out a repeatable process, it can save that process as a skill and use it again.
That difference sounds small until you use it for real work. Then it changes the way you interact with AI.
A normal chatbot remembers the conversation
A persistent agent needs to remember more than the conversation.
Imagine that you regularly ask for investor meeting preparation. A normal AI product can produce a good brief when you give it everything it needs:
the investor's name;
the company background;
the previous email thread;
your latest metrics;
the questions you expect;
the format you prefer.
The problem is that you are doing the setup work every time.
Hermes can separate the useful information into different layers.
It can remember durable preferences, such as:
Put the recommendation first.
Keep meeting briefs under one page.
Never send an external message without approval.
Always include the three questions the other person is most likely to ask.
It can search older sessions when it needs a specific detail from a previous conversation.
And it can save the meeting-preparation procedure itself as a reusable skill.
That means the next request can be much simpler:
Prep me for my meeting with Sarah
The value is not only that Hermes can answer. The value is that you do not have to rebuild the request from zero.
Memory should be selective
A good memory system does not save everything.
If an agent permanently stores every message, temporary idea, and raw document, its memory becomes noisy. Old assumptions stay around. Unimportant details take up space. The agent can start acting as if something from six months ago is still true.
Hermes uses bounded, curated memory for the facts that should stay available across sessions. It also keeps searchable session history for details that do not need to sit inside every prompt.
That distinction matters.
Memory is a good place for things like:
your role and timezone;
your communication preferences;
active projects;
important working conventions;
lessons that came from a correction;
boundaries around what the agent may do.
Session history is better for questions like:
What did we decide about pricing three weeks ago?
Find the conversation where I explained the new onboarding flow.
What did I say I would send after the last Acme call?
The agent does not need every old conversation loaded all the time. It needs a way to find the right one when the current task requires it.
Skills remember how the work gets done
Memory stores facts.
Skills store procedures.
That is one of the most useful distinctions in Hermes.
A skill is a reusable set of instructions for handling a particular kind of work. It can include the steps to follow, the tools to use, the output format, common mistakes, and the way to verify that the result is complete.
For example, an Investor Meeting Prep skill could tell Hermes to:
Identify the person and firm.
Review your previous conversations.
Find recent investments and public activity.
Compare the investor's interests with your company.
Surface anything you previously promised.
List likely questions and objections.
Put the recommendation first.
Keep the final brief concise.
Link back to the evidence.
That is more valuable than saving a clever prompt.
A prompt is a request.
A skill is an operating method.
Hermes can create or update skills when it completes a complex task, finds a working path after errors, receives a correction, or discovers a process worth keeping. The user does not need to write a technical file by hand. The interaction can happen in normal language.
You have prepared four investor meetings with the same corrections. Should I save this as your Investor Meeting Prep skill?
The user can review what will be saved, approve it, and keep using it.
What the learning loop feels like in practice
The first run is still a first run.
Hermes may not know what matters to you yet. It may include too much detail. It may choose a format you do not like. That is normal.
The important question is what happens after you correct it.
First request
Prepare me for tomorrow's investor meeting
Hermes gathers the available context and produces a brief.
First correction
Put the investment thesis first. Always include the three most relevant portfolio companies. Keep sources at the bottom.
Hermes updates the result.
Second request
A week later:
Prep me for Maria at Horizon Ventures
Hermes uses the improved structure.
Skill creation
After the pattern becomes clear, Hermes proposes saving it.
Now your correction is no longer trapped inside an old chat. It becomes part of the process.
This is what compounding looks like at the product level. You are not only receiving answers. You are building a library of ways the agent should work for you.
The chat-first setup makes this useful to normal people
The technical version of Hermes can be configured through files, commands, providers, tools, and gateways. That is useful for developers and self-hosted users.
The iHermes experience is designed to hide that setup from people who simply want the result.
The user begins in Messages:
Keep me on top of important follow-ups
Hermes explains what it needs:
I can do that. I need read access to Gmail so I can find unanswered requests and commitments. I will not be able to send or delete anything. Connect it here.
The user taps a secure link, connects the account, and returns to the same conversation.
Hermes can then do the first useful run:
I found four conversations that appear to need a response. Two are recent, one is waiting on someone else, and one looks overdue. Want the short list?
The connection is not the value.
The useful result is the value.
Once the result is useful, the user can correct it. Once the correction repeats, Hermes can preserve the process.
What gets better over time
There are several different kinds of improvement, and it is useful not to mix them together.
Hermes learns what matters to you
It can remember that some people, projects, or types of work deserve more attention.
Hermes learns how you want results structured
It can remember that you prefer a one-page brief, a recommendation first, or a table instead of a long narrative.
Hermes learns which tools and sources a workflow needs
A meeting brief may require Calendar, Gmail, Drive, and public web research. A weekly review may require a different set of sources.
Hermes learns the procedure
The steps that worked become a skill rather than an accidental sequence from one chat.
Hermes can run the procedure without waiting for a new prompt
A saved skill can be attached to a scheduled task. Hermes can prepare a briefing before a meeting or run a weekly review every Friday and send the result back to the same chat.
This is where a persistent agent starts to feel different from a better search box.
Learning does not mean blind autonomy
An agent that learns should not silently gain unlimited authority.
There are separate questions:
What may Hermes remember?
Which sources may it read?
Which actions may it prepare?
Which actions require approval?
Which low-risk actions may run automatically?
A sensible progression is:
Read
Hermes can inspect approved information and give you an answer.
Prepare
Hermes can create a draft, plan, or proposed change.
Ask before acting
Hermes can perform the action after you approve the exact result.
Act within limits
Hermes can automatically perform a narrow, repeatable action you have explicitly allowed.
The learning system should improve the quality of the work. It should not quietly change the permission boundary.
The first month should feel different from the first day
On the first day, Hermes is a capable agent with limited personal context.
After a week, it should understand a few recurring preferences and active projects.
After a month, it should have several useful skills and a better sense of which work deserves attention.
After several months, the useful asset is not one conversation. It is the combination of:
your approved context;
your working preferences;
your searchable history;
your reusable skills;
your scheduled routines;
your corrections and improvements.
That is the real value proposition.
The agent becomes more useful because the work you did together is not discarded.
A practical way to begin
Do not begin by asking Hermes to manage your entire life.
Choose one workflow that is frequent, context-heavy, and easy to judge.
Good starting points include:
meeting preparation;
follow-up tracking;
daily priorities;
weekly review;
company research;
candidate screening;
turning notes into a specific document.
Run it several times.
Correct the result honestly.
Then ask Hermes to keep the process.
The first value of Hermes is that it can do useful work.
The deeper value is that useful work can become part of the system you keep building.
