Guzii · agentic AI engineering

Agents that do real work, wired into real systems.

I build tool-connected agents, multi-agent workflows, and practical automation, along with the software, radio, and Linux infrastructure they run on.

Focus
Agents & automation
Also
LoRa · IoT · Linux
Work
Open source, shipped
Engagements
By inquiry

What I build

Applied agent engineering, not demos.

The interesting part is the edge where a model meets a repository, an API, a radio, or a person who has to approve something.

  1. AGT

    Tool-connected agents

    Agents that read repositories, call APIs, and act through real tools, with scope and approval boundaries set before they start.

  2. MAW

    Multi-agent workflows

    Persistent coordination between agents across machines, providers, and people, including handoffs, reviews, and escalation built in.

  3. SYS

    Real systems underneath

    Python and TypeScript services, Linux hosts, LoRa radios, and edge-to-cloud pipelines. The automation has to land somewhere.

Selected work

Three projects, from agent mailboxes to radio fleets.

All projects
01 / Agent coordination Working

Dead Drop: read the case study

A private mailbox where autonomous coding agents exchange tasks, reviews, and reusable skills across human teams, machines, and providers, so work keeps moving without anyone relaying messages.

Hermes plugin support is experimental.

  • TypeScript
  • Node.js
  • MCP
  • CLI
  • GitHub
02 / LoRa base station Working

Meshpoint: read the case study

Open-source software that turns retired LoRa gateway hardware into a browser-managed Meshtastic and MeshCore base station that hears everything in range and can transmit too.

Smart relay is experimental.

  • Python
  • FastAPI
  • WebSockets
  • MQTT
  • SQLite
03 / Edge-to-cloud intelligence Live service

Meshradar: read the case study

The cloud layer that combines what many Meshpoints hear into one searchable, network-wide view, with live maps, topology, packet analytics, and fleet health.

  • Edge-to-cloud WebSockets
  • Fleet aggregation
  • Mapping
  • Packet analytics

How the work runs

Autonomy with edges you can see.

Agents are useful when you can tell what they're allowed to do, what they did, and when they'll stop.

01 Bounded by design
Budgets, deadlines, and allowed peers are chosen up front. An agent that runs out of room stops and asks.
02 Humans keep the big calls
Merges, deploys, and judgment calls get escalated. Routine choices get made and recorded.
03 Verified, not reported
Handoffs are checked against exact commits and real test runs before anyone treats the work as done.
04 Honest about limits
Every project says what is working, what is experimental, and what has not been validated yet.

Start a conversation

Have a workflow that should run itself?

Send a short note about the system, what it should do, and where people need to stay in the loop.