# KubeCon NA 2026 — CFP Drafts

**Event**: KubeCon + CloudNativeCon North America 2026
**CFP window**: Typically opens June, closes August
**Watch**: https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/

Also consider co-located events:
- Multi-Cluster Management Day
- AI + Cloud Native Day
- Platform Engineering Day

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## Talk A — "The AI-Maintained Open Source Project: 90 PRs in Two Weeks"

**Session type**: Talk (35 min)
**Track**: Contributor Experience / Open Source Sustainability

### Abstract

What happens when you hand the majority of code authorship to AI agents?

KubeStellar is a CNCF-ecosystem multi-cluster Kubernetes project where the bulk of code is written, reviewed, and maintained by a system of AI agents — with humans setting direction and approving merges. In a two-week window in early 2026, over 90 pull requests were merged. Each was generated by a coding agent (Claude, Copilot, Gemini, or Codex), reviewed by an AI code-review agent, validated by automated tests and security scanners, and approved by a human maintainer.

This talk covers the architecture of the Hive multi-agent system: how agents are coordinated, how quality gates prevent regressions, what the bug-to-merge pipeline looks like end-to-end, and — critically — what humans actually do when AI handles volume. We'll share concrete metrics on velocity, defect rates, and contributor experience, and address the hard questions: Can you trust AI-written code? What breaks? Where does human judgment remain irreplaceable?

**Audience takeaways:**
- How to structure AI agent pipelines for open source projects
- Quality gates that make AI-authored PRs trustworthy
- What changes in project governance when AI is a first-class contributor
- The new role of human maintainers in an AI-assisted project

**Speakers**: Core KubeStellar maintainer team

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## Talk B — "Multi-Cluster AI Infrastructure: Running LLM Workloads at the Edge with KubeStellar"

**Session type**: Talk (35 min)
**Track**: AI + ML on Kubernetes / Edge Computing

### Abstract

Running AI workloads in production means solving a distribution problem: models need to run near data, across jurisdictions, at scale, without single-cluster bottlenecks. KubeStellar's multi-cluster orchestration engine — combined with its new A2A (Agent-to-Agent) and MCP (Model Context Protocol) server — provides the infrastructure layer that AI agent frameworks have been missing.

This talk walks through the architecture of running distributed AI workloads with KubeStellar, using the Cornell University Software-Defined Farm as a case study. We'll cover: how KubeStellar's placement engine routes AI workloads across clusters based on policy and data locality; how the A2A + MCP server lets AI agents coordinate across cluster boundaries; and what the operational reality looks like for a team managing inference workloads on edge hardware.

**Audience takeaways:**
- Multi-cluster deployment patterns for AI/ML workloads
- Using A2A + MCP for cross-cluster agent coordination
- KubeStellar placement engine: policy-driven workload distribution
- Case study: Cornell University SDF in production

**Speakers**: KubeStellar maintainer + Cornell University SDF team

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## Talk C — "Building a Production Multi-Cluster UI Without Writing Most of It"

**Session type**: Talk (25 min)
**Track**: Cloud Native UX / Platform Engineering

### Abstract

The KubeStellar Console is a production-grade multi-cluster management UI. It's also a case study in what's possible when you combine a clear plugin architecture, an AI-maintained codebase, and a community marketplace for extensions.

This talk shows what building and maintaining a complex Kubernetes UI looks like when the majority of code is AI-authored: how the plugin/card system enables rapid feature addition, how the community marketplace lets contributors extend the UI without touching core code, and how automated quality gates keep the AI-authored code trustworthy at scale.

Practical and demo-heavy. Attendees will leave with patterns applicable to their own platform tooling.

**Audience takeaways:**
- Plugin architecture patterns for extensible Kubernetes UIs
- AI-authored frontend code: what works, what doesn't
- Community marketplace model for UI extensions
- Live demo: the console in action

**Speakers**: KubeStellar Console maintainers

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## Submission Checklist

- [ ] CFP opens — monitor https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/
- [ ] Identify confirmed speaker(s) for each talk
- [ ] Finalize abstracts with speaker input
- [ ] Submit all 3 proposals (increases odds of at least one acceptance)
- [ ] Submit Talk A to Multi-Cluster Management Day CFP separately
- [ ] Submit Talk B to AI + Cloud Native Day CFP separately

Related: kubestellar/kubestellar#3789
