Hermes Agent Shows What Changes When AI Can Actually Do the Work

AI assistants have become very good at helping us think.

They can summarize information, write content, analyze data, generate code, explain unfamiliar subjects, and help work through decisions.

But there is still a large difference between an AI that can tell you how to do something and one that can actually do it.

That difference is what makes Hermes Agent interesting.

I have been experimenting with Hermes as a persistent AI agent rather than treating it as another chat interface. The experience has reinforced something I think product leaders should pay attention to:

Hermes is one example of what that transition can look like.

What is Hermes Agent?

Hermes Agent is an open-source AI agent designed to operate across tools, systems, and communication channels rather than simply responding inside a chat window.

It can run locally, on infrastructure such as a VPS, or through other deployment environments. It can use different AI models, execute tools, work with files, connect through MCP, maintain persistent memory, build reusable skills, and interact through platforms including Telegram, Slack, Discord, WhatsApp, and others.

Those capabilities individually are not completely new.

What becomes interesting is what happens when they are combined.

Instead of opening an AI application whenever you need help, the agent can become a persistent part of your environment.

You can communicate with it from somewhere you already work. It can remember relevant information from previous interactions. It can use tools to investigate something. It can perform multi-step work. It can learn procedures that are useful again later.

The interaction starts feeling less like using a chatbot and more like delegating work.

That distinction matters.

Memory changes the relationship with AI

One of the biggest limitations of traditional AI assistants has been continuity.

You have a useful conversation today, return a week later, and may need to explain much of the context again.

For simple questions, that is acceptable.

For ongoing work, it becomes a significant limitation.

Imagine having to remind a colleague about your projects, preferences, infrastructure, decisions, terminology, and previous work every time you spoke with them. They might still be intelligent, but working together would be exhausting.

Persistent agents need something closer to organizational memory.

Hermes includes built-in memory and can also integrate with external memory systems. Its memory architecture allows information to persist beyond a single conversation, while controls can determine whether memory is written automatically or requires approval.

That creates a much more interesting product problem than simply increasing a model’s context window.

These are product questions, not only technical ones.

The better AI becomes at remembering, the more important memory governance becomes.

Skills may matter as much as memory

Memory answers the question: What should the agent know?

Skills answer another question: What should the agent know how to do?

Hermes can create and improve reusable skills based on experience. It can also require approval before those skills are changed.

This is an important idea.

Most software has historically improved because a development team changed the product.

Agentic systems introduce another possibility. Some parts of the system can improve their behavior through use.

Suppose you repeatedly ask an agent to prepare a particular report.

Initially, you may need to explain where the information lives, how it should be analyzed, what should be excluded, and how the final result should be formatted.

Eventually, that process can become a reusable procedure.

The next time the request appears, the system does not necessarily need to rediscover the workflow from the beginning.

That sounds small, but it changes the economics of automation.

Traditional automation works best when a process is predictable enough for someone to define the workflow in advance.

Agents create the possibility that some workflows can become structured after the system has learned how the work is actually performed.

For product leaders, that opens interesting questions about where configuration ends and learning begins.

Tools turn intelligence into useful work

A capable model without tools can provide advice.

A capable model with tools can produce outcomes.

That is why integrations such as MCP matter so much to the agent ecosystem.

Hermes supports MCP servers as an additional way to give the agent access to external capabilities. The agent remains responsible for reasoning about the task, while MCP servers can contribute the tools it needs to interact with other systems.

This reinforces something I have written about before: products increasingly need to consider an agent-facing surface alongside their human interfaces and developer APIs.

An agent working on behalf of a customer may need to search information, inspect a system, update a record, create something, trigger a workflow, or coordinate several services.

The model itself does not provide those capabilities.

The surrounding product ecosystem does.

That is why I increasingly see AI product architecture as a combination of several layers:

Intelligence + context + memory + tools + permissions + experience

Remove one of those pieces and the value can fall quickly.

A brilliant model without business context does not know enough.

An agent with context but no tools cannot act.

An agent with powerful tools but poor authorization is dangerous.

An autonomous system with no visibility becomes difficult to trust.

The product is the combination.

Autonomy creates a trust problem

Giving an AI access to a terminal, APIs, business systems, or automation tools obviously introduces risk.

Hermes is interesting here because its architecture makes the tension visible.

Its security model includes controls for user authorization, dangerous-command approval, file operations, container isolation, MCP credentials, session isolation, and other protections. High-risk actions can require explicit human approval rather than executing automatically.

This points to a larger product principle:

Autonomy should not be a binary product setting.

We often talk about an agent as either autonomous or not autonomous.

Real products will need something much more nuanced.

Reading a public document may require almost no supervision.

Deleting production data should probably require considerably more.

Preparing a change could be autonomous while applying that change requires approval.

Sending an internal draft may be acceptable while communicating externally requires confirmation.

Different actions carry different consequences.

The role of product design is therefore not simply to maximize what the agent can do.

It is to determine what the agent should be allowed to do independently, what should require oversight, and how those boundaries remain understandable to the user.

The interface may become less important

Hermes also illustrates another shift I find interesting.

The AI experience does not necessarily need to live inside a dedicated AI application.

If an agent can operate through Telegram, Slack, a command line, an application interface, scheduled automation, or another existing channel, the conversational interface becomes portable.

That changes how we think about product distribution.

Historically, getting value from software usually meant going to the software.

You opened the application, navigated the interface, found the feature, entered information, and completed the task.

Agents can invert that relationship.

The user expresses intent where they already are, and software capabilities come to the user.

That does not mean interfaces disappear.

Complex configuration, review, approvals, monitoring, exception handling, and administration will continue to need thoughtful experiences.

But the interface may increasingly become a control surface for work performed by agents, rather than the only place where the work itself happens.

This is bigger than Hermes

Hermes is one project in a rapidly evolving agent ecosystem.

The specific tools will change.

Architectures will change.

Some projects that appear important today will disappear, while others will become platforms.

The broader direction is more durable.

Enterprise AI is already moving from generating answers toward performing delegated work. OpenAI has reported rapidly increasing agentic usage across functions outside engineering, while Google has described the challenge for organizations as moving agents from successful demonstrations into dependable production systems.

Once AI can act, the product conversation changes.

The question is no longer simply:

How intelligent is the model?

We need to ask:

• What can the system access?
• What does it remember?
• What tools can it use?
• What can it learn?
• What decisions can it make independently?
• Where does a person remain in control?
• How do we know what happened?

Those questions will determine whether agents remain impressive demonstrations or become dependable products.

What I take away from experimenting with Hermes

What interests me most about Hermes is not any individual feature.

It is the way the pieces start to come together.

Persistent memory gives the agent continuity.

Skills give it reusable ways of working.

Tools allow it to move from answering to acting.

MCP expands the systems it can work with.

Messaging channels make the agent accessible outside a dedicated application.

Security and approval controls create boundaries around autonomy.

Put those together and the experience begins to resemble something fundamentally different from the chatbot model that defined the first wave of generative AI.

We are moving toward software that does not simply wait for us to navigate through screens.

It can understand an objective, gather context, use available capabilities, perform work, remember what matters, and return when human judgment is required.

That is a much bigger product shift than adding an AI assistant to an existing application.

And for product leaders, that is why projects like Hermes are worth paying attention to.

They give us a place to experiment with the product questions we will increasingly need to answer as agents move from something we talk to into something we trust to work on our behalf.