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Platform Engineering in the Age of AI

Designing, Building, and Operating Modern Internal Developer Platforms for Cloud-Native and AI-Powered Workloads

Platform Engineering in the Age of AI
This book is 100% completeLast updated on 2026-08-25
Steve Publications

Platform engineering is changing fast, and AI is raising the stakes. This practical guide shows you how to build internal developer platforms that work in the real world, from cloud-native foundations to AI-powered workloads. Follow hands-on examples and a production-grade reference architecture to create platforms that are secure, scalable and built to evolve.

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About the Book

This book teaches you how to design, build, secure, operate, measure, scale, govern, and continuously evolve modern internal developer platforms that support both conventional cloud-native applications and AI-powered workloads. You will learn platform-engineering principles from first principles, progress through complete production-grade implementations, and understand how artificial intelligence is reshaping what platforms must provide and how they operate. The material assumes general software, cloud, or DevOps knowledge but explains specialized concepts thoroughly. Real code examples, step-by-step walkthroughs, and a progressive reference architecture guide you from foundational infrastructure to AI-enabled platform operations.

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Software ArchitectureBusiness ArchitectureBusiness StrategyPlatform EngineeringArtificial Intelligence

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About the Author

Steve Publications

Steve is a technology professional with over 20 years of experience in software development, server infrastructure, cybersecurity, vulnerability research and reverse engineering. He specializes in analyzing complex systems, identifying weaknesses and improving security.

Beyond his professional work, Steve collaborates with researchers and industry experts to create practical technical books covering software development, cybersecurity, cloud computing, DevOps, AI and enterprise technologies. His goal is to help IT professionals, students and enthusiasts develop practical skills through clear explanations, real-world examples and hands-on exercises.

At Steve Publications, we use AI to assist with translation, proofreading and editing but not to replace the research, technical expertise or hands-on experience behind our books. Some authors publish under pseudonyms for privacy or professional reasons.

Every book is written, reviewed and maintained by experienced professionals, supported by a private community of over 420 engineers and researchers. We prioritize technical accuracy, practical value and regularly updated content over mass-produced AI-generated text.

We welcome experienced professionals interested in writing or reviewing books, especially in specialized and emerging technology fields. If you'd like to contribute, contact us with your area of expertise.

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Contents

Table of Contents

Designing, Building, and Operating Modern Internal Developer Platforms for Cloud-Native and AI-Powered Workloads

Introduction: Why Platform Engineering Matters Now

  1. What You Will Learn
  2. How This Book Is Organized
  3. The DevForge Reference Platform
  4. Technology Choices and Trade-offs
  5. Assumptions and Prerequisites
  6. Version Sensitivity and Rapidly Changing Landscapes
  7. A Note on AI Hype and Critical Evaluation

Chapter 1: The Platform Engineering Imperative

  1. From Chaos to Craft: How Software Delivery Got Complex
  2. DevOps, SRE, and the Seeds of Platform Thinking
  3. The Cognitive Load Crisis in Modern Engineering
  4. What Platform Engineering Is and Is Not
  5. How AI Changes the Platform Equation

Chapter 2: Platform-as-a-Product

  1. Treating Developers as Customers
  2. Platform Teams and Operating Models
  3. Developer Personas and User Research
  4. Platform Metrics That Matter: DORA, SPACE, and Beyond
  5. Maturity Models and Adoption Strategies

Chapter 3: Cloud-Native Foundations

  1. Containerization and the Reproducible Runtime
  2. Kubernetes Architecture and Core Concepts
  3. Cloud Infrastructure Patterns and Multi-Account Design
  4. Networking, DNS, TLS, and Ingress
  5. Storage, Databases, and Shared Services

Chapter 4: Infrastructure as Code and GitOps

  1. Declarative Infrastructure and State Management
  2. Terraform Modules and Workspace Patterns
  3. GitOps Principles and Controllers
  4. Argo CD Implementation Walkthrough
  5. Drift Detection, Policy Enforcement, and Rollback

Chapter 5: CI/CD and Delivery Pipelines

  1. Pipeline Architecture and Design Principles
  2. GitHub Actions Implementation Walkthrough
  3. GitLab CI and Jenkins Alternatives
  4. Artifact Management and Supply Chain Security
  5. Environment Promotion Strategies

Chapter 6: Developer Portals and the Paved Road

  1. The Developer Portal as Platform Front Door
  2. Backstage Implementation Walkthrough
  3. Service Catalogs and Ownership Metadata
  4. Golden Paths and Software Templates
  5. Reducing Cognitive Load Through Abstraction

Chapter 7: Platform Security and Governance

  1. Zero Trust for Internal Platforms
  2. Identity Federation and Workload Identity
  3. Secrets Management Patterns
  4. Policy as Code with OPA and Kyverno
  5. Software Supply Chain Security and SBOMs

Chapter 8: Observability and Reliability Engineering

  1. The Three Pillars Plus Events
  2. OpenTelemetry Implementation Walkthrough
  3. Metrics, Dashboards, and Alerting with Prometheus and Grafana
  4. SLOs, SLIs, and Error Budgets for Platforms
  5. Incident Management and Post-Incident Learning

Chapter 9: FinOps and Cost Engineering

  1. Unit Economics of Cloud Infrastructure
  2. Kubernetes Resource Efficiency and Rightsizing
  3. Showback, Chargeback, and Developer Transparency
  4. Capacity Planning and Autoscaling Strategies
  5. Storage and Network Cost Optimization

Chapter 10: AI Foundations for Platform Engineers

  1. From Traditional ML to Foundation Models
  2. Large Language Models: Capabilities and Limitations
  3. Embeddings, Vector Search, and Semantic Retrieval
  4. Retrieval-Augmented Generation Architecture
  5. Model Serving, Inference Infrastructure, and APIs

Chapter 11: Building AI Platform Capabilities

  1. Self-Service Model Access and AI Gateways
  2. GPU and Accelerator Infrastructure Management
  3. RAG Services and Vector Database Operations
  4. Model Deployment and Evaluation Pipelines
  5. Token Economics and Inference Cost Controls

Chapter 12: Agentic Platform Engineering

  1. Agent Architectures and Tool Calling
  2. Permissions, Identity, and Least Privilege for Agents
  3. Human-in-the-Loop Controls and Approval Gates
  4. Multi-Agent Workflows and Orchestration
  5. Failure Containment, Rollback, and Safety Patterns

Chapter 13: AI-Assisted Platform Operations

  1. AI in the Development Lifecycle
  2. Infrastructure as Code Generation and Review
  3. Incident Triage and Root Cause Analysis with AI
  4. Log Analysis, Anomaly Detection, and Capacity Forecasting
  5. When Deterministic Automation Beats AI

Chapter 14: Platform Governance, Testing, and Lifecycle

  1. Comprehensive Platform Testing Strategies
  2. AI Evaluation Pipelines and Release Gates
  3. API Versioning and Backward Compatibility
  4. Platform Upgrades and Migration Patterns
  5. AI Governance Frameworks and Controls

Chapter 15: Enterprise Adoption and Architectures

  1. Organizational Assessment and Context Analysis
  2. Build-Versus-Buy Decisions Across the Platform Stack
  3. Team Structures and Operating Models
  4. Phased Implementation Roadmaps
  5. Anti-Patterns and Pitfalls to Avoid

Conclusion: Building Platforms That Endure

Platform Engineering in the Age of AI Reference Blueprint

  1. Organizational Assessment Guide
  2. Platform Product Strategy
  3. Team Design and Operating Model
  4. Cloud and Kubernetes Foundations
  5. Repository Structure
  6. Platform APIs and Custom Resources
  7. Golden Paths for Common Workloads
  8. Security Architecture Summary
  9. Observability Architecture Summary
  10. FinOps Architecture Summary
  11. AI Platform Architecture Summary
  12. Phased Implementation Roadmap
  13. Production-Readiness Checklist
  14. Security Checklist
  15. AI Governance Checklist
  16. Troubleshooting Guide
  17. Glossary
  18. References and Bibliography

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