Agentic Platform Engineering represents the next evolution in infrastructure and service management—transforming platform teams from reactive ticket-takers into proactive product builders. By combining AI-powered development tools with modern platform engineering practices, we're enabling teams to enhance both their internal developer platforms and portals that deliver exceptional experiences to development teams that in turn deliver measurable business value.
This repository demonstrates how platform engineering teams can leverage AI agents, specifically with GitHub Copilot, to accelerate the delivery of cloud-native infrastructure and services on Azure, while maintaining security, governance, and operational excellence.
Agentic Platform Engineering extends traditional "platform engineering" by integrating AI-powered agents and automation throughout the platform lifecycle. It builds upon established practices including:
- Cloud Native Architecture - Leveraging containers, microservices, declarative APIs, and immutable infrastructure
- DevOps & DevSecOps - Shifting left on security, automation, and quality throughout the development lifecycle
- GitOps & Infrastructure as Code - Managing infrastructure through version-controlled, declarative configurations
- Platform as a Product - Treating internal platforms as products with developers as customers and business outcomes as measures of success
- Self-Service & Golden Paths - Empowering development teams with curated, secure, and compliant patterns
Traditional platform engineering often involves repetitive tasks: writing boilerplate IaC, updating documentation, responding to common questions, reviewing PRs for compliance, and managing service catalogs. Agentic platform engineering introduces AI agents that augment platform engineers, enabling them to:
- Generate production-ready infrastructure code from natural language requirements
- Automatically maintain documentation as infrastructure evolves
- Proactively suggest optimizations and cost improvements
- Accelerate code reviews with security and compliance checks
- Scale platform expertise across the organization
Platform engineering teams today face several critical challenges:
Most organizations aren't starting fresh. They have:
- Legacy procurement processes and vendor relationships
- Existing "pet" infrastructure running critical workloads
- Compliance requirements built around outdated tooling
- Teams with varied skill levels and resistance to change
- Technical debt accumulated over years of reactive firefighting
Meanwhile, business expectations are accelerating:
- Faster time-to-market for new features
- Enhanced security posture and compliance
- Cost optimization and observability
- Developer productivity and satisfaction
- Consistency across multi-cloud and hybrid environments
Platform teams are stretched thin:
- High demand for platform features vs. limited engineering capacity
- Knowledge silos and single points of failure
- Context switching between strategic work and operational toil
- Difficulty scaling platform expertise as the organization grows
This repository demonstrates a solution pattern that can be adapted across different tools and cloud providers:
- Source Control & Collaboration - Centralized version control, pull request workflows, and team collaboration
- AI-Powered Development Tools - Code generation, documentation, testing, and review automation
- Cloud Infrastructure Platform - Scalable, secure compute, storage, networking, and managed services
- Infrastructure as Code (IaC) - Declarative infrastructure definitions with state management
- CI/CD Pipelines - Automated testing, security scanning, and deployment workflows
- Policy as Code - Automated compliance and governance enforcement
- Observability & FinOps - Monitoring, logging, cost tracking, and optimization
Each solution follows a consistent structure:
Problem Scenario → Solution Pattern → Specific Implementation
We start with real-world platform engineering challenges, present a generalized solution approach, then provide concrete implementations using:
- GitHub Enterprise - Enterprise-grade source control and collaboration
- GitHub Copilot - AI pair programmer and agent capabilities
- Azure - Cloud infrastructure and platform services
- Terraform - Industry-standard Infrastructure as Code
- Additional ecosystem tools - Security scanning, policy enforcement, observability, and more
This content is designed for platform engineering practitioners who want to:
- Elevate their platform engineering practices with AI-powered workflows
- Implement self-service infrastructure patterns with golden paths
- Scale platform capabilities without proportionally scaling team size
- Bridge the gap between legacy systems and cloud-native architecture
- Demonstrate measurable business value from platform investments
While platform engineers are the primary audience, keep in mind the value chain:
- Immediate consumers: Development teams using your platform
- End value consumers: The business benefiting from faster, more secure, more reliable software delivery
Your success is measured by developer productivity, application reliability, security posture, and ultimately business outcomes.
This repository is organized to support different presentation needs and learning paths:
- Problem Scenarios - Real-world challenges platform teams face
- Solution Patterns - Generalized approaches applicable across tool stacks
- Reference Implementations - Production-ready examples using GitHub, GitHub Copilot, and Azure
- Presentations - Slide decks tailored for different audiences (executives, practitioners, developers)
- Demos - Hands-on examples demonstrating agentic platform engineering in action
(Coming soon)
(Coming soon)
This project is licensed under the terms specified in the LICENSE file.