Why Every Platform Team Shouldn't Build Their AI Standards From Scratch
The article discusses the inefficiencies of platform engineering teams independently creating AI standards from scratch. It highlights the redundancy in rediscovering common practices and suggests a layered model for shared standards. By adopting a composable approach, teams can build on existing knowledge and focus on unique customizations.
- ▪Many platform engineering teams are reinventing the wheel by independently developing AI coding standards.
- ▪The article proposes a layered model for shared standards to reduce redundancy and improve efficiency.
- ▪The first layer consists of industry-wide best practices, while subsequent layers include organization-specific standards and team customizations.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3897065) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Luigi Di Fraia Posted on Apr 28 Why Every Platform Team Shouldn't Build Their AI Standards From Scratch #ai #agents #automation #devops This is Part 0 of a series on building agentic AI workflows for platform engineering. Part 1 jumped straight into the practical how-to: your first steering file, the workspace structure, getting started with Terraform.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV Community.
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