WG AI Integration Charter

WG AI Integration Charter

This charter adheres to the conventions described in the Kubernetes Charter README and uses the Roles and Organization Management outlined in wg-governance.

Scope

The AI Integration Working Group focuses on enabling seamless integration of AI/ML control planes with Kubernetes, as well as providing standardized patterns for deploying, managing, and operating AI applications at scale on Kubernetes.

The Working Group will provide a forum for a broad engineering community to give feedback to the project on challenges encountered when integrating with Kubernetes.

This addresses a broad need with many end-users deploying complex AI systems, AI/ML platform providers, Kubernetes distributions, and developers of distributed AI applications facing these integration challenges. Standardizing solutions in this space benefits the entire Kubernetes ecosystem. Adjacent ecosystems could link to the outputs of this WG as a trusted vehicle for supporting AI integrations with Kubernetes.

In scope

  • Develop a shared community point of view and associated best practices enabling AI agent (or multi-agent) systems to integrate with Kubernetes.

  • Provide a forum for intersecting code experimentation in AI integration space and discussion with the existing Kubernetes community.

  • Recommend an appropriate go forward governance model for AI Integrations with the Kubernetes project.

  • Identify appropriate auth(z) patterns for AI connector identities, its closest caller, and Kubernetes RBAC.

  • Defining benchmarks on pros/cons of design approaches to meet user outcomes.

  • Ensure security, observability, and policy enforcement can be consistently applied across integrated systems (K8s and external Control Planes such as LLMs) and AI integration applications.

  • Define potential enhancements to API conventions to scale AI integration patterns that respect data privacy and safety concerns during our design process. Consider alternative API patterns that could be a better fit for AI enablement.

  • Explore patterns for efficient network access to emergent protocols such as MCP/A2A via proxies or gateways.

  • Reduce the complexity and custom development required for deploying, building and managing connectors of kubernetes API with AI agent ecosystems.

Out of Scope

  • Development of AI/ML frameworks or applications
  • General-purpose workload management not specific to AI/ML
  • Deploying inference workloads on Kubernetes (which is covered by WG Serving)
  • Manage accelerator devices (which is covered by WG Device Management)

Deliverables

  • The WG will provide space for collaboration and experimentation. If/when any solid ideas emerge that require changes to Kubernetes (for example, updates to kubectl for AI consumption), the WG will facilitate and coordinate the delivery of KEPs and their implementations by the participating SIGs.
  • Interim artifacts will include documents capturing use cases, requirements, integration architecture designs, and AI application communication patterns.
  • Establish best practices document for AI tool integration with Kubernetes and a clear recommendation if/what set of reference tools may best fit in Kubernetes project itself informed from data driven experimentation with appropriate governance model.

Stakeholders

  • SIG Architecture
  • SIG API Machinery
  • SIG Apps
  • SIG Auth
  • SIG CLI

Roles and Organization Management

This working group adheres to the Roles and Organization Management outlined in wg-governance and opts-in to updates and modifications to wg-governance.

Exit Criteria

The WG is done if/when a shared recommendation is in place for how the Kubernetes project should or should not integrate with these emergent systems. This could include a recommendation for Kubernetes to adopt and/or evolve tools (e.g. MCP connectors, benchmark or environment validation tooling, etc.) and evolve its own governance model to provide proper stewardship within the project or outside.

The working group will disband when the KEPs resulting from these discussions have reached a terminal state. When the core functionality for AI workload management reaches GA, we will evaluate whether the working group should be disbanded and any remaining KEPs be left to the management of their owning SIGs.

View Charter