*# How I Built an Agentic Layer on AIRTIMES Using Hermes Agent

A submission for the Hermes Agent Challenge

What is AIRTIMES?

AIRTIMES is a zero-dependency global data and service network I built from scratch — a full-stack Node.js backend featuring AirPay (a crypto payment module) and AirBank (a revenue tracking engine). No external libraries. No cloud lock-in. Every byte of it runs on infrastructure I control.

It's the kind of platform that's always generating data — transactions, node health events, revenue streams — but has no brain to interpret that data in real time. That's exactly the gap I wanted to close.

The Problem: Infrastructure Without Intelligence

AIRTIMES processes payments and tracks revenue across nodes. But monitoring it meant either:

• Staring at logs manually
• Writing one-off scripts that break and never improve
• Paying for a SaaS dashboard that doesn't understand my custom data model

None of those felt right. What I needed was an agent that could live inside the platform, understand its data, react to events, and — crucially — get smarter the longer it ran.

That's when I found Hermes Agent by Nous Research.

Why Hermes?

Most agent frameworks are graphs you design ahead of time. You wire the nodes, define the flows, and ship. The agent's capability is permanently bounded by what you prompted into it on day one.

Hermes is different. It has a built-in learning loop:

• After completing a complex task, it automatically writes a reusable skill
• Skills self-improve every time they're used
• It builds a persistent memory of your projects across sessions
• It runs unattended automations via natural language cron scheduling

For AIRTIMES — a platform that runs 24/7 — this is exactly the right architecture.

What I Built: The AIRTIMES Agentic Layer

I installed Hermes directly on the AIRTIMES server and gave it three jobs.

1. AirPay Transaction Monitor

Hermes watches the AirPay transaction stream continuously. When a payment fails, it:

1. Diagnoses the failure (network timeout? invalid address? insufficient funds?)
2. Triggers the retry logic
3. Logs a structured incident report
4. Sends a Telegram alert to my phone

The first time it did this, it wrote a skill called airpay_failure_diagnosis.skill automatically. The next failure? It handled in seconds — no manual input.

2. AirBank Daily Revenue Briefing

Every morning at 8am, Hermes pulls from the AirBank revenue engine and delivers a plain-language summary:

📊 AIRBANK DAILY BRIEF — May 20, 2026
Revenue: $4,820 (+12% vs yesterday)
Top node: Node 3 ($1,240)
Anomaly: Node 7 — 0 transactions since 03:00 UTC
Action: Pinging Node 7 now...

Set up with one command:

hermes cron "every day at 8am run airbank_daily_brief"

No dashboard. No manual query. Just a brief, every morning, in my Telegram.

3. Multi-Platform Command Surface

I connected Hermes to Telegram so I can command the entire AIRTIMES infrastructure in plain language from my phone:

"What's total AirPay revenue this week?"
"Is Node 7 online?"
"How many transactions failed in the last 24 hours?"

Hermes fetches, reasons, and responds. It works on Telegram, Discord, Slack, and CLI — all from a single gateway process.

The Self-Improving Loop in Action

This is what separates Hermes from every other agent I've used.

After three days of running on AIRTIMES, Hermes had automatically generated 6 custom skills:

I didn't write any of these. Hermes wrote them itself — and every subsequent run of the same task was faster and more accurate because the skill had been refined.

Installation (60 seconds)

# Install Hermes
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

# Run setup wizard
hermes setup

# Connect Telegram
hermes setup --gateway telegram

# Point it at your project
hermes chat "I have a Node.js server running AIRTIMES on port 3000. 
Monitor transaction health and report anomalies."

What This Changes

AIRTIMES used to be infrastructure I managed. Now it's infrastructure that manages itself — with me as the oversight layer, not the execution layer.

Hermes doesn't just execute tasks. It accumulates operational knowledge about AIRTIMES: which nodes fail most often, what payment patterns precede failures, which revenue anomalies are noise vs. signal. That knowledge compounds.

Six months from now, the version of Hermes running on AIRTIMES will be meaningfully smarter than the version I installed today — without me touching a line of code.

That's the promise of a self-improving agent. And for a global infrastructure platform like AIRTIMES, that's not a nice-to-have. It's the only architecture that makes sense.

What's Next

• Expanding Hermes to manage AIREXPRESS (EV mesh connectivity dashboard)
• Building a public agentskills.io skill pack for AIRTIMES operators
• Exploring batch trajectory generation for training a domain-specific AIRTIMES model

Links

• 🔗 Hermes Agent
• 🔗 GitHub — NousResearch/hermes-agent
• 🔗 Hermes Agent Challenge

Built with Hermes Agent by Nous Research · MIT License

Tags: #hermesagentchallenge #devchallenge #agents #opensource
is a submission for the Hermes Agent Challenge*