agents-at-scale-ark

agents-at-scale-ark

Provider-agnostic operations for agentic resources. ARK codifies patterns and practices developed across dozens of agentic application projects.

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ARK is an agentic runtime for Kubernetes that codifies patterns and practices developed across client projects. It provides a foundation for platform-agnostic operations and standardized deployment approaches. The project is in early access, evolving based on team feedback, and aims to share technical approach with the community for feedback and input in the field of agentic AI systems and Kubernetes orchestration.

README:

⚒️ ark

Agentic Runtime for Kubernetes

Technical Preview & RFC. Part of the Agents at Scale Ecosystem

Run agentic workloads across any system or cluster.


QuickstartDocumentation

CI/CD npm version PyPI version

Quickstart

Clone the repository and run the quickstart:

git clone https://github.com/mckinsey/agents-at-scale-ark.git
cd agents-at-scale-ark
make quickstart

User guides, developer guides, operations guides and API reference documentation is all available at:

https://mckinsey.github.io/agents-at-scale-ark/

We are transitioning this project to our open-source repository. During this process we will be moving documentation URLs, artifact registries, etc. Some builds, artifacts and pipelines may be impacted.

What is ARK?

ARK codifies patterns and practices developed across dozens of client agentic application projects. These projects span multiple sectors, functions, and technology stacks. Through this experience, we identified recurring challenges around platform-agnostic operations for agentic resources and the need for standardized deployment and management approaches.

This project represents the distillation of those learnings into an open-source runtime. While in early access and rapidly evolving based on ongoing team feedback, ARK provides a foundation built on real-world production experience with agentic systems at scale.

Technical Preview

Agents at Scale - Agentic Runtime for Kubernetes ("Ark") is released as a technical preview and early access release. This software is provided as a Request for Comments (RFC) to share elements of our technical approach with the broader technology community, gather valuable feedback, and seek input from practitioners and researchers in the field of agentic AI systems and Kubernetes orchestration.

As a technical preview release, this software may contain incomplete features, experimental functionality, and is subject to significant changes based on community feedback and continued development. The software is provided "as is" without warranties of any kind, and users should expect potential instability, breaking changes, and limited support during this preview phase.

Credits

The initial design and implementation of Ark was led by Roman Galeev, Dave Kerr, and Chris Madden.

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