The Platform Engineer’s Handbook • Ajay Chankramath & Kaspar von Grünberg • GOTO 2026

2026-07-02 · Source: GOTO Conferences · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

The "Platform Engineer's Handbook" by Ajay Chankramath and Kaspar von Grünberg offers a hands-on guide for building a production-ready Internal Developer Platform (IDP) from scratch. The book addresses the current inflection point in platform engineering, where the discipline has matured beyond niche practices but faces new challenges with AI, which can amplify existing problems if foundational systems are weak. It provides practical, runnable code examples, capturing hard-won lessons from building and sponsoring platforms across organizations, with a strong emphasis on adoption. The content progresses from core foundations like Kubernetes and service mesh to self-service capabilities and enterprise-grade features, including compliance as code and FinOps, culminating in AI readiness. The book is industry-agnostic, enterprise-focused, and utilizes 100% open-source tools, reflecting the demanding effort required to create such a practical resource.

Key takeaway

For platform engineers and AI teams building or evolving internal developer platforms, prioritizing robust, open-source foundations is critical. Your investment in a product-oriented platform approach, encompassing core infrastructure, self-service, and enterprise-grade features like compliance as code, will directly determine your ability to safely and productively integrate AI agents. Neglecting these foundations risks amplifying existing issues rather than realizing AI's promised productivity gains.

Key insights

Platform engineering, viewed as a product, is crucial for building robust, vendor-agnostic foundations essential for effective AI integration.

Principles

• Platform engineering is product thinking for DevX.
• Solid foundations prevent AI from amplifying problems.
• Treat platform building as continuous investment.

Method

Build core Kubernetes-based foundations, incrementally add capabilities, enable self-service via developer portals, then integrate enterprise-grade features like compliance as code and FinOps for AI readiness.

In practice

• Implement Kubernetes with service mesh and OIDC.
• Develop self-service developer portals.
• Apply compliance as code and FinOps practices.

Topics

• Platform Engineering
• Internal Developer Platform
• AI Agents
• Developer Experience
• Cloud Sovereignty
• Open-Source Software
• Enterprise Architecture

Best for: Software Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by GOTO Conferences.