Enterprise AI agents, built for production.

An agent that works in a demo and an agent that works in your business are two different things. The demo has to impress once. The production system has to be right thousands of times, on your real data, inside your real permissions, where a wrong action has a cost.

I build agents for that second world. They plan across multiple steps, call your systems through typed and permissioned interfaces, remember what happened, and record every decision to an append-only log you can hand to an auditor. If an agent can't show its work, it doesn't ship.

What an enterprise agent actually needs

Three cores have to be solid before autonomy is safe. Skip any one of them and you get a system that's impressive in a demo and dangerous in production.

• A tooling core — typed, permissioned interfaces to your CRMs, ticketing, databases, and internal APIs, with scoped permissions and deterministic fallbacks when a call fails.
• A retrieval core — grounded retrieval over your documents and records so answers and actions are based on your reality, not the model's best guess.
• An oversight layer — every plan, tool call, and decision traced and logged, with human checkpoints on the actions that warrant them.

Where agents earn their keep

The highest ROI is high-volume, multi-step work that follows clear rules but eats human hours: operations automation, research and synthesis, customer operations, and internal copilots that sit on top of your own knowledge. I look for the process where an agent moves a real number — hours saved, cost per task, error rate — and I start there.

An agent that can't prove what it did is a liability, not a productivity gain. I build agents that do the work and keep the receipts.