Jan Zorz
Jan Zorz is the Head of ProVision Labs, Principal Architect & Innovation Lead, working on the development of network protocols and automation solutions to accelerate the global deployment of IPv6. A dedicated internet community leader with over 30 years of experience, Jan serves as the Vice Chair of the RIPE Programme Committee, Chair of the RIPE SEE regional meeting, and is the founder of SINOG. He is the primary co-author of RIPE-554 (the global standard for IPv6 procurement) and author of several IETF RFCs. Jan is also a co-founder and Board Member of the Global NOG Alliance (GNA), actively driving its "Keep Ukraine Connected" initiative. Previously, he worked with the Internet Society and served as CEO of the Go6 Institute. Jan is based in Slovenia, EU, and has presented his work at all five Regional Internet Registries and over 150 technical conferences worldwide.
Session
Anycast is easy to explain and notoriously difficult to observe. Announce the same IP address from multiple locations, allow BGP to choose a path, and traffic should reach the "nearest" instance, although BGP's idea of nearest often has little to do with geography.
ProVision (formerly 6connect) operates an anycast DNS platform spanning six prefixes, 39 BGP nodes, 13 regions, and five continents. For years, we knew the platform worked because queries were answered. What we could not prove was how it worked: which users reached which nodes, how quickly packets arrived, and where traffic quietly disappeared.
This presentation tells the story of how we turned one of anycast's most frustrating behaviors-ICMP replies returning to the "wrong" node-into a measurement. That insight grew into an automated pipeline running 117,000 probes per second across 2.6 million targets, measuring latency, loss, jitter, routing behavior, path MTU failures, ECMP paths, and visibility at 845 Internet Exchanges.
It is also the story of how two network engineers who are not full-time developers built the system through intensive collaboration with AI coding models. AI supplied much of the implementation capacity; the humans supplied decades of network intuition, architectural direction, validation, and the authority to say when the code was confidently wrong.