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Introduction

What is CAIPE (Community AI Platform Engineering)​

💡 Tip: CAIPE (Community AI Platform Engineering), pronounced like cape (as in a superhero cape 🦸‍♂️🦸‍♀️). Just as a 🦸‍♂️ cape empowers a superhero, CAIPE empowers platform engineers with 🤖 Agentic AI automation! 🚀

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.

Intro

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
  • ☸ Kubernetes Agent for K8s ops
  • 💬 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

Goals of the project​

  • Enable Platform Engineering teams with a curated, validated set of persona-specific multi-agent systems (MAS) tailored to their unique enterprise requirements.

  • Foster an ecosystem of AI Platform Engineering practitioners to collaboratively develop high-quality, reusable prompt engineering libraries and commonly used platform tools.

  • A carefully curated library of meta-prompts, continuously evaluated for effectiveness with both our agents and the MCP server to drive optimal performance in agentic workflows.

Who we are​

We are Platform Engineers, SREs, and Developers from a variety of companies in the CNCF and CNOE.io ecosystems, passionate about open source and advancing the use of agentic AI in Platform Engineering.