Confidential Computing Summit 2026

AI agents are making decisions, calling tools, and talking to other agents, often with access to sensitive data they shouldn't be able to see in plaintext. The usual answer is to just trust the infrastructure, but that falls apart in multi tenant clouds and cross org workflows. This talk covers what it actually takes to run agentic AI workloads inside Trusted Execution Environments. I'll walk through the architecture for isolating agent-to-agent communication using confidential VMs, how attestation works when agents need to dynamically invoke external tools, and the gotchas we hit around key management and session state. We'll look at real performance numbers and what the overhead looks like on GPU backed inference inside TEEs and where the bottlenecks actually are. Attendees will leave with a concrete reference architecture for deploying AI agents with hardware-rooted trust boundaries, plus practical guidance on attestation flows for multi-party agent pipelines. If you're building agentic systems that handle regulated or sensitive data, this talk gives you a starting point that doesn't require rearchitecting everything from scratch.