AgentOS
AgentOS is a control plane for running AI agents inside a company: roles, teams, approvals, channels, memory, and runtime isolation managed from one dashboard. Operators can create and govern the fleet without touching the runtime underneath. I designed and built the product, backend, operator console, and AWS infrastructure solo.
- Role
- Solo Founder + Lead Engineer
- Period
- 2025 to present
- Status
- Production
How the system fits together.
The calls that shaped it.
- 01
The core decision was to build a control plane, not a wrapper. Operators create agents, hand them skills, watch what they do, and shape their environment from the dashboard — the runtime stays an implementation detail. That one boundary shaped everything else.
- 02
Nothing risky happens unsupervised. Every action that reaches a real outside system passes a verification agent (GateKeeper) and, when it matters, a human — and it fails safe: if the gate is down, the action is blocked, not waved through.
- 03
You can describe a team in plain English and the platform designs it for you — agents, skills, schedules, channels — as a blueprint you review and approve before anything is created. Standing up a new set of agents feels like a conversation, not a config project.
- 04
A marketplace of skills and plugins with trust built in: agents can pull the safe, everyday capabilities themselves, sensitive ones need an admin’s sign-off, and every item tracks who made it.
- 05
It’s a real operations product, not a demo: live fleet monitoring, cost tracking that separates infrastructure from model spend, full audit history, Microsoft Teams / Slack / Telegram reachability, and a monitoring agent that watches the other agents — all deployed as infrastructure-as-code.
The interesting work isn't the stack. It's the boundaries.
What it runs on.
- 01 Python / FastAPI control plane — one service, separate doors for the dashboard and the agents
- 02 Next.js operator console for creating, watching, and governing the fleet
- 03 Agents run in isolated AWS Fargate containers on the OpenClaw runtime, each with a gateway that keeps it in sync
- 04 A verification agent (GateKeeper) in front of every external write
- 05 AI blueprints that design whole fleets or single agents, with human approval before anything is built
- 06 A marketplace for skills and plugins with per-item trust tiers
- 07 Microsoft Teams bots provisioned automatically through Azure, plus Slack and Telegram
- 08 Qdrant vector memory scoped by agent, team, and org
- 09 AWS, infrastructure-as-code across four CDK stacks: network, data, platform, agents