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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.

AI Platform Engineering project provides a customizable, secure, enterprise-ready and cloud deployable reference multi-agent system that streamlines platform operations through persona-driven “usecase agents” such as Platform Engineer, Incident Engineer, and Product Owner etc. Each usecase agent 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 persona agents:

• 🚀 ArgoCD Agent for continuous deployment
• 🚨 PagerDuty Agent for incident management
• 🐙 GitHub Agent for version control
• 🗂️ Jira Agent for project management
• 💬 Slack Agent 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