I’ve been seeing “AI agents” everywhere lately. Every other tool claims it can automate work, plan tasks, or act like a smart assistant. But most of them look impressive in demos and then feel pretty limited in real use.
So instead of just watching another video or reading marketing claims, I decided to actually use Hermes Agent for a full day and see what it does with real work.
Not toy prompts. Not “write me a poem.”
Just normal tasks I usually handle myself.
First impression
It didn’t feel like a normal chatbot.
Most AI tools wait for you to ask something and then respond. Hermes Agent felt a bit more active in comparison — like it was trying to break the task into steps instead of answering in one go.
Also, since it’s open-source and can be run on your own setup, it gives more control than most closed AI tools. That already makes it interesting for developers.
What I tested
I basically used it like a small assistant for the day. I gave it tasks like:
Researching topics
Summarizing long text
Planning simple workflows
Breaking down multi-step problems
Organizing ideas
Helping with small coding tasks
Keeping context across tasks
My main goal was simple: see if it actually saves time in real work.
Research and summarization
I started with long content and asked it to summarize and organize it.
What I noticed was that it didn’t just shorten everything. It tried to structure the information in a more readable way.
It wasn’t perfect, but it was useful enough that I didn’t feel like I had to rewrite everything from scratch.
Workflow planning
This part was more interesting.
I asked it to plan a small workflow with multiple steps.
Where many AI tools struggle is structure — they either oversimplify or lose consistency midway.
Here, it did better. It broke the task into clear parts like:
what needs to be done
smaller steps
order of execution
basic flow of the process
It actually felt like it was thinking through the task instead of just responding.
Context and memory
One thing I noticed was that it handled context better than basic chat tools.
It wasn’t perfect memory, but during the session it did refer back to earlier inputs in a more natural way than I expected.
That makes a difference when you’re working on multiple related tasks instead of isolated prompts.
Where it struggled
It wasn’t smooth everywhere.
There were moments where:
it repeated similar points
longer tasks lost some clarity
results depended heavily on how clearly I wrote the prompt
sometimes the depth wasn’t consistent
So it definitely still needs proper guidance.
What stood out
The main thing I noticed wasn’t speed or output quality.
It was the behavior.
Instead of just answering questions, it often tried to process the task step-by-step. That small shift makes it feel less like a chatbot and more like a task-based system.
That’s probably the biggest difference compared to normal AI tools.
Open-source factor
Another plus point is that it’s open-source.
That means you can:
run it locally or on your own setup
modify it
connect different tools
experiment freely
For developers, that flexibility is a big deal.
Final thoughts
After using Hermes Agent for a full day, I wouldn’t call it perfect or fully “agent-like” in a real autonomous sense.
But it’s also not just another chatbot with a fancy label.
It sits somewhere in between — still developing, but clearly moving toward more structured, workflow-based AI.
The biggest change I felt was simple:
Instead of just chatting with an AI, it felt more like giving tasks to a system that tries to execute them step by step.
That direction feels more useful than hype.
Disclosure
This write-up is based on the user’s hands-on testing experience with Hermes Agent. The content has been structured and refined for clarity and readability using AI assistance.
Top comments (4)
Hey, this article appears to have been generated with the assistance of ChatGPT or possibly some other AI tool.
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Thank you for pointing this out.
I've updated the article and added an AI disclosure to comply with the guidelines.
Just to clarify, the project featured in this post is entirely my own work, and the experiences, challenges, and story shared in the article are based on my personal journey rebuilding AlgoPair for the Finish-Up-A-Thon challenge. I wrote the article myself, but I did use AI tools to help improve the wording, structure, grammar, and overall readability of the post.
The ideas, project details, and reflections are all my own, while AI was used only as a writing assistant to help present them more clearly.
Thanks again for the reminder, and I've made sure the post now includes the appropriate disclosure.
Great explanation
Amazing stuff