Most people still use AI as a better chat window. Ask something, get an answer, copy the result somewhere else, open another application, and do the next step manually.
That is useful, but agents become far more interesting once they can actually use tools.
This is what interests me about Hermes Agent. Instead of being limited to conversation, the agent can work with files, APIs, repositories, applications, browsers, messaging platforms, memory, and other services.
At that point, the question changes from what can AI tell me? to what can I delegate to it?
These are the use cases I find most useful.
One Interface for My Tools
The first thing I want from a personal agent is a single interface to everything else. I should be able to talk to it through Telegram, a terminal, or a web UI and ask things like:
- What do I have today?
- Which emails actually need my attention?
- Create a task from this message.
- Find the note where I discussed this idea.
- Check whether the deployment is healthy.
- Research this before my meeting.
- Remind me about this next week.
- What did I leave unfinished yesterday?
The agent does not need to replace the applications behind all of this. It becomes the coordination layer over them.
I don’t want another productivity app that gives me another inbox to maintain. I want the agent to work with the tools where my information already lives.
Coding
Software development is one of the strongest agent use cases because a lot of the work can be inspected and verified.
An agent can help with:
- Exploring unfamiliar repositories.
- Explaining architecture.
- Finding where a change belongs.
- Turning requirements into implementation plans.
- Writing code.
- Running tests.
- Reviewing implementations.
- Reviewing pull requests.
- Finding edge cases.
- Updating dependencies.
- Checking release notes for breaking changes.
- Maintaining documentation.
- Preparing releases.
You can also split the workflow between several agents. One can plan the task, another implement it, another review the change, and another run QA.
I don’t think every workflow needs ten agents talking to each other simply because multi-agent systems sound interesting. Specialized agents are useful when they create actual separation of responsibility.
A developer agent writing the implementation and another agent independently checking whether it satisfies the original requirement makes sense. Five agents endlessly discussing a trivial change usually does not.
DevOps and Operations
Agents can also become a useful interface over infrastructure. Instead of only sending an alert when something fails, an operations agent can gather context before bothering me.
It could watch:
- Failed deployments.
- Application errors.
- Background jobs.
- Disk usage.
- Database connectivity.
- API latency.
- Certificate expiry.
- Backup failures.
- Queue backlogs.
- Unexpected infrastructure cost.
If an application starts throwing errors, I don’t just want:
API health check failed.
I want something closer to:
Errors started six minutes after the latest deployment. Most failures are coming from the payment webhook handler and contain the same database constraint error.
That is much more useful because the agent has already done the first part of the investigation. It can also prepare deployments by running tests, checking environment variables, verifying migrations, deploying to staging, and validating health checks.
I would still keep production and destructive operations behind explicit approval.
Research
Research is another area where agents make a lot of sense. Instead of giving one giant question to a model and trusting the first answer, an agent can break the problem into smaller questions, search multiple sources, compare claims, track publication dates, and produce a cited result.
I find this useful for:
- Technical research.
- Framework and library evaluation.
- Product research.
- Competitor monitoring.
- Interview preparation.
- Market research.
- Tool comparisons.
- Tracking something that changes regularly.
The agent can also continue monitoring a topic and only tell me when something meaningful changes, which removes a lot of repeated research.
Personal Knowledge
This is probably one of the areas I care about most.
My information is spread across notes, conversations, repositories, project documents, bookmarks, journals, tasks, and random ideas. I don’t want to remember where everything lives.
I want to be able to ask things like:
What did I decide about this project?
Where did I write about this idea?
I’ve discussed this several times. How has my thinking changed?
What projects have I repeatedly postponed?
What decisions are still unresolved?
This is where persistent memory becomes genuinely useful, but memory should not mean saving every conversation forever and stuffing all of it back into the model.
Useful information should become durable context:
- Preferences.
- Project information.
- Important decisions.
- Recurring workflows.
- Writing guidelines.
- Repository conventions.
- Lessons from previous work.
Temporary noise should remain temporary.
Reusable Skills
One of the things I like most about Hermes is the idea of converting successful workflows into reusable skills.
If I keep asking the agent to perform the same task in roughly the same way, eventually I should not have to explain the process again. A skill can define:
- When it should run.
- What input it needs.
- Which tools it can use.
- What steps it follows.
- How it validates the result.
- Which actions require approval.
- What happens when something fails.
That is far better than continuously expanding one giant prompt. The agent becomes more useful because it accumulates reliable workflows rather than simply accumulating more instructions.
Writing and Content
AI is also useful around the writing process without having to become the actual author.
An agent can:
- Research a topic.
- Find supporting material.
- Organize rough notes.
- Build an outline.
- Detect repetition.
- Check facts.
- Improve metadata.
- Find outdated articles.
- Suggest internal links.
- Prepare image assets.
- Turn a long article into smaller formats.
- Create a publishing draft or pull request.
The important part for me is preserving my own writing style. I want AI to help with research, structure, editing, and repetitive work without making every article sound like generic AI content.
Email and Calendar
Email and calendar contain a ridiculous amount of repetitive admin.
An agent can summarize important email, identify messages that need replies, extract commitments, draft responses, prepare meeting briefs, find free time, and tell me what actually needs attention today.
A useful morning brief might look like:
- Three meetings today.
- Two emails need replies.
- One task is overdue.
- A production backup failed overnight.
- You have a two-hour focus window between 2 and 4 PM.
That is more useful than opening four applications before I even know what matters.
I would start with read access and keep sending email, inviting people, or modifying important events behind confirmation.
Personal Admin
A lot of boring personal administration can also be delegated. For example:
- Recording expenses.
- Extracting data from receipts and invoices.
- Finding recurring subscriptions.
- Checking upcoming renewals.
- Tracking deliveries.
- Maintaining shopping lists.
- Organizing files.
- Renaming documents.
- Preparing travel information.
- Processing PDFs.
- Summarizing long documents.
None of these is individually revolutionary. The interesting part is having one agent coordinate all of them instead of spreading the work across a dozen separate apps and routines.
Browser and Desktop Automation
Not every useful system has an API. Sometimes the agent needs to interact with a website or desktop application the same way a person would.
That opens up things like:
- Filling repetitive forms.
- Downloading reports.
- Uploading files.
- Testing websites.
- Checking application status.
- Moving information between systems.
- Taking screenshots.
- Operating internal tools.
- Managing local files and applications.
I would prefer direct APIs and command-line tools whenever possible because they are easier to control and verify. Browser and GUI automation is useful when there is no better interface.
Scheduled Work
A persistent agent is also useful because I don’t need to remember to ask it for everything.
It can run scheduled jobs such as:
- Morning briefs.
- Weekly reviews.
- Dependency monitoring.
- Website checks.
- Newsletter digests.
- Backup verification.
- Infrastructure reports.
- Competitor monitoring.
- Content audits.
- Subscription reviews.
This is where an agent running continuously on a server becomes very different from opening ChatGPT whenever I remember that I need something. The work can happen while my laptop is closed and the results can simply reach me when they are useful.
I wrote separately about what I’d use a Mac mini server for, including persistent agents, local models, automation, development services, and other always-on workloads.
Permissions Matter
Once an agent has tools, permissions become part of the architecture.
A research agent does not need production credentials. A coding agent does not need my personal email, and an expense assistant should not be able to initiate payments.
I generally think of actions in three groups.
Safe to Automate
- Reading approved files.
- Searching information.
- Creating summaries.
- Running tests in a sandbox.
- Preparing drafts.
- Checking system health.
Needs Confirmation
- Sending email.
- Publishing content.
- Inviting people to meetings.
- Opening important pull requests.
- Making purchases.
- Changing external systems.
Keep Manual
- Moving money.
- Deleting production data.
- Changing critical security settings.
- Rotating important credentials.
- Signing agreements.
- Destructive production operations.
The exact boundaries depend on the system, but unrestricted access is a terrible default. Important actions should also be logged so I can see what the agent changed, which tools it used, and what happened if something went wrong.
Why Hermes Works for Me
What I like about Hermes is that it is not just a chat interface sitting in front of an LLM. The useful part is the combination of persistent context, reusable skills, tools, scheduled work, and the ability to keep running on a machine or server.
That allows the same agent to gradually become useful across very different areas without forcing every workflow into one gigantic conversation. Some tasks need memory, some need tools, some need scheduled execution, and some are better turned into reusable skills once the process becomes stable.
The platform itself is not the interesting part to me. What matters is whether it actually removes work from my life.
Start With One Useful Workflow
I would not start by giving an autonomous agent access to everything I own. The sensible approach is to pick one repetitive, low-risk workflow that is easy to verify and make that reliable first.
It might summarize newsletters, check deployments, search notes, prepare a morning brief, or handle some other task I currently repeat manually. Once that works consistently, it can become a reusable skill and I can move on to the next workflow.
Over time, those small workflows start connecting to each other. The same agent already knows where my information lives, which tools it can use, what I care about, and which actions need approval.
That is the point where it stops feeling like another chatbot and starts becoming something genuinely useful: an interface to the tools and information I already use every day.