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As Platform Engineering, SRE, and DevOps environments grow in complexity, traditional approaches often lead to delays, increased operational overhead, and developer frustration. By adopting Multi-Agentic Systems and Agentic AI, Platform Engineering teams can move from manual, task-driven processes to more adaptive and automated operations, better supporting development and business goals.

Community AI Platform Engineering (CAIPE) (pronounced as cape) is an open-source, Multi-Agentic AI System (MAS) championed by the CNOE (Cloud Native Operational Excellence) forum. CAIPE provides a secure, scalable, persona-driven reference implementation with built-in knowledge base retrieval that streamlines platform operations, accelerates workflows, and fosters innovation for modern engineering teams. It integrates seamlessly with Internal Developer Portals like Backstage and developer environments such as VS Code, enabling frictionless adoption and extensibility.

CAIPE is empowered by a set of specialized sub-agents that integrate seamlessly with essential engineering tools. Below are some common platform agents leveraged by the MAS agent:

• 🚀 ArgoCD Agent for continuous deployment
• 🚨 PagerDuty Agent for incident management
• 🐙 GitHub Agent for version control
• 🗂️ Jira/Confluence Agent for project management
• 💬 Slack/Webex Agents for team communication

...and many more platform agents are available for additional tools and use cases.

Together, these sub-agents enable users to perform complex operations using agentic workflows by invoking relavant APIs using MCP tools. The system also includes:

• A curated prompt library: A carefully evaluated collection of prompts designed for high accuracy and optimal workflow performance in multi-agent systems. These prompts guide persona agents (such as "Platform Engineer" or "Incident Engineer") using standardized instructions and questions, ensuring effective collaboration, incident response, platform operations, and knowledge sharing.
• Multiple End-user interfaces: Easily invoke agentic workflows programmatically using standard A2A protocol or through intuitive UIs, enabling seamless integration with existing systems like Backstage (Internal Developer Portals).
• End-to-end security: Secure agentic communication and task execution across all agents, ensuring API RBACs to meet enterprise requirements.
• Enterprise-ready cloud deployment architecture: Reference deployment patterns for scalable, secure, and resilient multi-agent systems in cloud and hybrid environments

For detailed information on project goals and our community, head to our documentation site.

AI Platform Engineer can handle a wide range of operational requests. Here are some sample prompts you can try:

• 🚨 Acknowledge the PagerDuty incident with ID 12345
• 🚨 List all on-call schedules for the DevOps team
• 🐙 Create a new GitHub repository named 'my-repo'
• 🐙 Merge the pull request #42 in the ‘backend’ repository
• 🗂️ Create a new Jira ticket for the ‘AI Project’
• 🗂️ Assign ticket 'PE-456' to user 'john.doe'
• 💬 Send a message to the ‘devops’ Slack channel
• 💬 Create a new Slack channel named ‘project-updates’
• 🚀 Sync the ‘production’ ArgoCD application to the latest commit
• 🚀 Get the status of the 'frontend' ArgoCD application

• Quick Start Guide
• Setup
  • Docker Compose
• Local Development setup
• Run Agents for Tracing & Evaluation
• Adding new agents

We’d love your contributions! To get started:

1. Fork this repo
2. Create a branch for your changes
3. Open a Pull Request—just add a clear description so we know what you’re working on

Thinking about a big change? Feel free to start a discussion first so we can chat about it together.

• Browse our open issues to see what needs doing
• New here? Check out the good first issues for some beginner-friendly tasks

We’re excited to collaborate with you!

Licensed under the Apache-2.0 License.

Made with ❤️ by the CNOE Contributors