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Assaf
Barnir
Head of Global Technical Support
Sentry
Assaf Barnir is Head of Global Technical Support at Sentry, where he's currently operationalizing AI without breaking customer trust. His customers are engineers — which means the bar for "good enough" is set by the same people who built the tools he's deploying. Tough crowd. He's done stints at Zendesk, PubNub, RapidAPI, Netomi and more — long enough to know that the answer to most support problems isn't more headcount, it's better architecture. He runs on a personal operating principle called GIFOC: Get In Front of Customers. Everything else is noise. He also wrote Unscared: AI to his Aunt — because she was, well, scared.
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22 September 2026 09:45 - 10:15
Shadow running AI: The framework for safer AI adoption
As organizations race to implement AI in customer support, many are unintentionally testing new systems in front of the people they can least afford to disappoint: their customers. While AI can accelerate efficiency and scale, moving too quickly into production can create risk around accuracy, trust, and customer experience. The challenge is no longer simply deploying AI, but introducing it in a way that protects both customers and the business. In this interactive keynote, Assaf Barnir, Head of Global Support at Sentry, will share an engineering-driven approach to evaluating AI safely through a “Shadow Run” model. Discover how organizations can test AI alongside human teams, identify operational blind spots before they become customer issues, and establish the right signals for moving from experimentation to production with confidence. What this session will cover: - Why AI initiatives often struggle when experimentation happens directly with customers - How a “Shadow Run” model can reveal operational risks before rollout - The signals and success metrics that indicate production readiness - Turning AI evaluation into stronger feedback loops across Support, Product, and GTM teams Key takeaway: Attendees will leave with a practical framework for validating AI safely, reducing implementation risk, and moving from experimentation to production with greater confidence.