For 21 years, my work was focused on designing distributed systems and products that handle large volumes of data.
Early on, I worked on high-availability and replication for network-attached storage at EMC, where I learned how to make complex distributed systems reliable.
After that, I spent a few years at SAP, focused on large-scale enterprise data, processes, and systems.
Learnings from these early years apply directly to my work now on autonomous AI agents that collaborate across networks and companies to automate tedious business processes.
In 2014, I became CTO of a hardware company that was transitioning into a platform for the Internet of Things. I designed that system and led the deployment of thousands of sensors and controllers embedded in city infrastructure, ports, and more worldwide.
Many of our sensors were machine-learning based and operated in extremely noisy environments, like city streets with power lines, buses, and interference, which taught me how to ship reliable products on top of probabilistic and non-deterministic behavior, a challenge that's acute in all AI products.
Because these systems were embedded in critical infrastructure, like cities and ports, security and trust had to be foundational, not bolted on.
That experience led me to build Ockam, a collection of protocols and an open-source Rust library for cryptographic identities, trust, and end-to-end encrypted communication in distributed systems.
Autonomy brings together all those experiences to empower teams to ship autonomous products that are dependable, secure, and highly scalable.