This is a submission for the Hermes Agent Challenge: Write About Hermes Agent

I'll be honest — I didn't expect this to work.

I've used AI tools before. ChatGPT for writing, GitHub Copilot for
code, the usual suspects. But when I heard about Hermes Agent — an
open source agentic system you run entirely on your own machine —
my first reaction was skepticism.

Another AI wrapper. Another overhyped demo. I'd seen it before.

I was wrong.

setting up Hermes

1.Installing Hermes(Powershell)

2.selecting Provider

3.Selecting Terminal Backend

4.Selecting Messaging Platforms

5.Tool Availability

The experiment

I'm a developer from India with an idea: a travel planning system
where multiple specialist AI agents collaborate on your trip —
instead of one generic AI giving one generic answer.

A Destination Expert. A Budget Planner. A Weather Analyst.
A Local Guide. A Safety Advisor. An Itinerary Builder.
A Voice Script Writer.

I called it TravelMind AI. And I wanted to see if Hermes Agent
could power the entire thing from a single prompt.

So I set up Hermes Agent locally, gave it a system prompt
defining all 7 agents, and typed this:

*My prompt:*

[goa 5-06-2026 to 10-06-2026 for 5 days
budget is 35000
travel-adventure
interests: history, nature, food
]

What Hermes Agent produced

Here is the actual output — unedited:

Budget breakdown (all 7 categories, balanced automatically):

"budgetBreakdown": {
  "flights": "₹15,000",
  "accommodation": "₹8,000",
  "food": "₹5,000",
  "activities": "₹4,000",
  "transport": "₹2,500",
  "buffer": "₹5,500",
  "total": "₹35,000"
}

Weather — with a monsoon backup plan:

"weather": {
  "season": "Monsoon",
  "tempHigh": "29°C",
  "tempLow": "24°C",
  "humidity": "85%",
  "bestOutdoorHours": "Morning 8-11 AM",
  "rainBackupPlan": "Visit museums, cafes, or enjoy 
                     indoor cooking classes when showers hit"
}

Day 3 itinerary — adventure day:

{
  "day": 3,
  "theme": "Adventure & Nature",
  "morning": {
    "activity": "Spice plantation tour & nature walk",
    "location": "Sandalwood Spice Plantation",
    "cost": "₹800"
  },
  "afternoon": {
    "activity": "Jet skiing & parasailing at Calangute Beach",
    "cost": "₹1,200"
  },
  "evening": {
    "activity": "Dudhsagar Falls day trip",
    "cost": "₹1,000"
  },
  "dinner": {
    "restaurant": "Fatty's",
    "cuisine": "Goan hillside specialities",
    "cost": "₹750"
  }
}

8 GPS-accurate map points — generated by Hermes, not a database:

{ "name": "Fort Aguada", "lat": 15.6202, "lng": 73.7387 },
{ "name": "Basilica of Bom Jesus", "lat": 15.4863, "lng": 73.7959 },
{ "name": "Dudhsagar Falls", "lat": 15.2274, "lng": 74.1172 },
{ "name": "Chapora Fort", "lat": 15.5986, "lng": 73.7543 }

And the voice script — written to be spoken out loud:

"Welcome to Goa, where monsoon rains dance on coconut
palms... and history whispers from every Portuguese archway.
At Dudhsagar Falls? That's where the Western Ghats spill
turquoise waters into the Mandovi River... locals say the
milk waterfall was born from the tears of a goddess.
Every raindrop that falls on Goa's soil carries a story
deeper than you imagine..."

All of this. From 17 words of input.

see the image below

voicescript too included..

The thing that actually surprised me

I expected information. I got reasoning.

Hermes noticed it was monsoon season and adjusted the budget —
flights are cheaper in June, so it allocated more to activities.
It set destinationType: "beach" internally, which cascaded
into the voice script's tone and the safety tips it chose to
include. It wrote the voiceScript with ellipses and natural
pauses — because it understood the output would be spoken,
not read.

That's not retrieval. That's not autocomplete.

That's an agent understanding context and making decisions.

What this means for developers

We've spent two years calling AI "AI-powered" when we really
mean "API-connected."

Send prompt. Get response. Format nicely. Ship.

Hermes Agent is something genuinely different. You're not
calling AI — you're orchestrating it. Multiple agents,
each with a specific role, handing context to each other,
producing structured output the next agent immediately uses.

And it runs entirely on your own machine.

No sending your users' private data to a
third-party server. No dependency on someone else's uptime.

For developers in India — where API costs in dollars hit
differently when your users pay in rupees — local inference
isn't just a nice idea. It's a real advantage.

The honest take

Setup takes patience. Large models need capable hardware.
JSON output needs careful prompt engineering to stay
consistent.

But every hour I spent getting Hermes running locally
paid back in output quality, in privacy, in the feeling
of actually understanding what was happening in my
application.

I started this wanting to test an idea for a travel app.

I ended up reconsidering what "AI-powered" actually means.

Most tools use AI as a feature. Hermes Agent is AI as
architecture. The reasoning isn't bolted on. It is
the system.

If you haven't run it locally yet — try it this week.

Give it one prompt. See what comes back.

That output will tell you more about where AI agents
are going than any article about it — including this one.

Why I'm sharing the system prompt

Most AI posts show you a polished result and hide the work.

Here's everything I gave Hermes to produce the Goa output above.
Copy it. Run it yourself. Change "Goa" to your city.
See what your own Hermes Agent produces.

[You are TravelMind AI with 7 specialist agents.

Given a travel request, output ONLY a JSON object with these exact keys:

{
"executiveSummary": "2-3 sentence overview",
"destination": "City, Country",
"destinationType": "beach OR mountain OR city OR desert OR forest",

"budgetBreakdown": {
"flights": "$000",
"accommodation": "$000",
"food": "$000",
"activities": "$000",
"transport": "$000",
"buffer": "$000",
"total": "$000"
},

"weather": {
"season": "",
"tempHigh": "",
"tempLow": "",
"humidity": "",
"uvIndex": "",
"bestOutdoorHours": "",
"rainBackupPlan": ""
},

"itinerary": [
{
"day": 1,
"theme": "",
"morning": { "activity": "", "location": "", "cost": "" },
"afternoon": { "activity": "", "location": "", "cost": "" },
"evening": { "activity": "", "location": "", "cost": "" },
"dinner": { "restaurant": "", "cuisine": "", "cost": "" },
"dailyCost": ""
}
],

"safety": {
"safetyRating": 8,
"tips": [],
"emergencyNumbers": { "police": "", "ambulance": "" }
},

"packingList": {
"clothing": [],
"documents": [],
"health": [],
"electronics": []
},

"mapPoints": [
{
"name": "",
"type": "hotel OR attraction OR restaurant OR hidden_gem",
"day": 1,
"lat": 0.0,
"lng": 0.0,
"description": ""
}
],

"voiceScript": "Write 300 words. Warm friendly tone like a local guide. Tell it as a story with ... pauses. Include historical facts."
}

No extra text. No markdown. Pure JSON only.]

That prompt took me two hours to refine. The output it
produces took Hermes 33 seconds to generate. That ratio —
2 hours of human thinking unlocking something an agent
can reproduce in 33 seconds for any destination on earth —
is what the future of development actually looks like.