CNCF Reveals KubeCon 2026 Schedule with New AI Tracks
The Cloud Native Computing Foundation (CNCF), which builds sustainable ecosystems for cloud native software, today announced the full schedule for KubeCon + CloudNativeCon North America 2026, taking place November 9-12 in Salt Lake City, Utah. This year’s program introduces a new AI Inference + Agentic track alongside established platform engineering and security programming, reflecting the growing role of Kubernetes in running production AI systems and operating infrastructure at scale.
As AI moves beyond experimentation and into production, the focus is turning to the infrastructure required to reliably support inference-heavy and agent-driven workloads at scale. The CNCF Annual Cloud Native Survey shows that Kubernetes has become a foundational platform for production workloads, with 82% of container users running it in production and 66% of organizations using generative AI workloads relying on it. As AI systems place greater demands on underlying platforms, the need for scalable, production-grade infrastructure and efficient inference has never been more urgent.
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“AI is quickly becoming one of the largest compute workloads the industry has ever seen, and the shift from training models to running them in production is where the real engineering challenge lives now,” said Jonathan Bryce, executive director, CNCF. “Kubernetes wasn’t built for AI, but this community has spent years hardening it for exactly this kind of distributed, always-on workload. This year’s KubeCon + CloudNativeCon North America program brings together the people actually solving that problem in production.”
Highlights at KubeCon + CloudNativeCon North America 2026 include:
AI Inference + Agentic
Running generative AI and agentic systems in production requires rethinking cluster scheduling, GPU utilization and latency limits. The new AI Inference + Agentic track delves into practical patterns for orchestrating autonomous agents, optimizing model serving with tools like vLLM and KServe, and implementing dynamic routing and observability across inference pipelines.
Platform Engineering
Platform teams are being asked to make internal platforms more usable, more extensible and easier to operate as cloud native adoption scales, driving interest in projects and practices such as Backstage, Argo and GitOps workflows that can reduce friction without slowing engineering velocity. The Platform Engineering track focuses on the internal platforms, automation and operational practices that help teams support software delivery at enterprise scale.
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Security
Distributed, ephemeral, and automated cloud native environments create challenges for managing supply chain security, identity, and runtime protection, leading teams to increasingly adopt eBPF observability, policy enforcement and secure software development practices. The Security track covers the practices and controls needed to protect infrastructure across detection, identity and credential management, multi-tenancy, confidential computing and vulnerability management, with attention to technologies such as Cilium, eBPF and OpenTelemetry.
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