Platform Engineering Evolution: From Custom Scripts to Self-Service
A Wardley map showing the evolution of platform engineering capabilities, from basic scripts to sophisticated developer platforms.
Explore all content tagged with "Platform Engineering" across insights, frameworks, and resources.
RSS FeedAI is shifting from code generation copilots to agentic systems that execute scoped tasks, while data platforms and infra teams are building the governance and “system maps” (metadata, service...
AI agents are rapidly becoming a production workload, forcing a new CTO playbook: optimize token/tool spend, build internal agent platforms, and pair scale with governance, reliability, and...
AI agents are becoming first-class production workloads—and the differentiator is shifting from model choice to governed execution: sandboxed runtimes, identity-aware access to enterprise systems,...
AI adoption is shifting from ad-hoc tooling to an outcome-driven operating model: teams are standardizing AI in the dev workflow (PRs, automation), defining success metrics and guardrails, and...
The week’s pattern: “AI everywhere” is forcing grown-up operating systems
AI is entering its “reliability era”: companies are building agentic capabilities with deterministic guardrails, sandboxed execution, and explicit success metrics—treating AI as a governed platform...
Teams are transitioning from “trying AI” to running AI as a first-class production workload: building shared AI platforms, optimizing GPU utilization, embedding vector search into core data systems,...
AI delivery is shifting from isolated copilots to always-on, real-time, governed “agentic + RAG” systems—forcing CTOs to treat data streaming, vector search, schema governance, and automated security...
Team Topology Designer guide: team topologies designer tool for 10 to 100 engineers
AI is moving from experimentation to production optimization: teams are simultaneously optimizing inference throughput, standardizing AI-enabled engineering workflows, and choosing between RAG and...
AI is moving from experimentation to disciplined operations: teams are investing in production-grade AI engineering skills, adopting agent/tool-calling patterns, and reshaping operations and...
Engineering orgs are rapidly standardizing “agentic AI” as a first-class production workload—building internal agent platforms, adding CI/CD for data+AI pipelines, and tightening the operational...
Have experience to share? We welcome contributions from technical leaders.
Learn how to contribute