Every project on this page was designed, built, and deployed alongside autonomous AI agents. From 33-agent swarms that build software on their own, to voice assistants that answer phones — this is what happens when you stop treating AI like a tool and start treating it like a team.
See the work ↓A sampling of production applications — each one designed, coded, audited, and deployed with AI agents as collaborators. Sorted by what they actually do, not what buzzwords they use.
A self-operating software company. Customers describe what they need in plain English. Fifteen AI agents — each with a distinct role — take it from concept to deployed application without a single human developer writing code. Aria chats with the customer. Patricia writes the design walkthrough. Oscar orchestrates the build. Bob and his crew write the code. Andrew's team audits every line. David deploys it live. Ronnie watches over all of it.
The system works through precise orchestration — fifteen agents synchronized in a pipeline where each one picks up exactly where the last one left off. A customer describes what they want in plain English. Within seconds, one agent is extracting requirements while another is already drafting the technical design. The moment the design is approved, a build orchestrator breaks it into parallel tasks and dispatches them across a team of builders working simultaneously. As code comes in, a separate squad of auditors reviews every line before it's accepted. Once everything passes, a deployment agent pushes it live — no human developer in the loop. A watchdog agent monitors the entire pipeline end to end, catching bottlenecks, restarting stalled work, and escalating edge cases that need a human decision. The result: a customer goes from "I need an app that does X" to a live, deployed application — built, reviewed, and shipped by agents working together like a real software team.
Not "AI-assisted" in the way most people mean it. These applications were architectured, coded, reviewed, and deployed by autonomous agents — with a human directing the vision and making the decisions that matter. The AI writes the code. I make sure it solves the right problem.
Not every project uses every tool. But this is what's in the toolkit — and it's all production-tested.
There's a difference between using AI to autocomplete code and building systems where AI agents run the entire operation. Here's how I think about it.
I don't use AI to suggest the next line of code. I architect teams of autonomous agents — each with a defined role, clear responsibilities, and the ability to collaborate with each other. They build. I direct.
Every project here is deployed and running on real infrastructure. Not prototypes. Not demos. Production applications serving real users, with real uptime requirements and real consequences when something breaks.
Voice AI, cybersecurity, stock analysis, property management, education, consumer apps — the diversity isn't accidental. AI-native development means the bottleneck isn't technical skill in one domain. It's the ability to understand problems clearly and orchestrate solutions.
I don't rent someone else's platform and hope it works. I run my own server, manage my own systems, and control how every piece talks to every other piece. When something breaks at 2am, I don't file a support ticket — I fix it. That's the difference between building on top of something and actually understanding it.