Service
Enterprise Agentic AI
Multi-agent systems that carry real work.
Specialized agents that research, decide, act across your tools, and check their own work — grounded in your data, inside limits a regulated business can defend.
What this is
The hard decisions in agentic AI are architectural, and you make almost all of them before you pick a model. Which agents exist. What each one is allowed to touch. How they hand off, what context they get, how their output is judged, and where a person still has to sign. I’ve built this shape of system in production: a hierarchy of agents inside a Fortune-500 financial institution that read design files, write against the firm’s own component libraries, and test their own output before it reaches human review. Since then, an MCP server doing speaker-attributed search across millions of meeting-transcript segments. Agents that review pull requests. Agents that run sprint operations. Anyone can get a demo working. I build for the Tuesday it becomes load-bearing.
Why it’s worth a premium
Co-inventor on a filed patent for that architecture. It went into production and it’s still there, doing the work, inside a regulated institution.
What you get
- Multi-agent orchestration
- Tool-using, action-taking agents
- Grounded retrieval and MCP servers
- Guardrails and human-in-the-loop
How it fits the method
The AI Maturity Journey
AI Curiosity
Experiments
AI Automation
Copilots
Agentic Systems
Autonomy
AI-Native Organization
Leverage
Autonomous Enterprise
Compounding
The assessment takes about five minutes and gives you your stage, your real constraint, and the next move worth funding. If you’d rather talk it through, take thirty minutes with me. You’ll get a real answer either way, whether or not we end up working together.