Quarkus at KubeCon North America 2026

[insectengine]

Event Description: CNCF’s flagship conference brings together the open source communities advancing cloud native computing and the infrastructure behind modern AI systems.

Date: November 9th - 12th , 2026
Location: Salt Lake City, Utah • USA
Event Type: In-Person

https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/

Wednesday Sessions

Session: Many Agents, One Platform: Production Multi-Agent Apps on K8s with CNCF Building Blocks
Speaker(s): Daniel Oh
Date/Time: Wednesday at 1:20 PM in Salt Palace | Level 1 | Hall A-C, Hall 1-5
Abstract: Multi-agent setups are easy to sketch on a whiteboard and painful to run on a cluster. If you've watched agents chase each other in circles or ship traces that stop at the model boundary, you already know why "more agents" isn't the hard part, but the operations are. Daniel Oh focuses on the boring-but-important layers such as isolation and scaling on Kubernetes, handoffs with CloudEvents, tracing/metrics with OpenTelemetry across model calls and tools, plain gRPC/HTTP between services, guardrails with OPA, SPIFFE/SPIRE where identity actually matters, and KEDA when work piles up asynchronously. Then we run a live Kubernetes demo using cloud-native microservices (Java/Quarkus-style) wiring multiple agents together end-to-end, with traces you can actually follow in Grafana or Jaeger. Walk away with a short implementation checklist, a handful of failure modes worth catching early, and a simple mental model for keeping multi-agent systems maintainable without bolting on bespoke glue.

Thursday Sessions

Session: From Laptop to Cluster: The Agentic AI Inner Loop on Kubernetes
Speaker(s): Kevin Dubois & Laurent Broudoux
Date/Time: Thursday at 11:00 AM in Salt Palace | Level 2 | 255 EF
Abstract: Building agentic AI systems means juggling API keys for LLMs and MCP-connected tools, plus the burden of configuring those services correctly. Add expensive inference calls and highly customized dev setups, and the inner loop quickly becomes inner hell.

This session will demonstrate a streamlined inner-loop experience for agentic AI. We will develop, test, and iterate on multi-agent systems on our laptop, then deploy to Kubernetes with zero code changes. We'll show how to auto-provision local LLM inference, build agents with typed tool contracts, compose workflows, and test interactions with hot reload. We'll also show how MCP and/or A2A enable portable integration and separation of concerns, and how teams can switch to production-grade inference through configuration alone, with Kubernetes-native health checks, observability, and deployment patterns.

You'll leave understanding how to build and ship AI systems with open-source tooling and cloud native patterns you already know.