Showing category results for Platform Engineering

Agentic Platform Engineering for SaaS.  Accelerating the Path to Market with Git-APE If you are an ISV building SaaS on Azure, the first demo is usually the easy part. The harder work is turning that proof of concept into a secure, scalable, commercial-ready service that can be operated confidently in production. That gap shows up quickly. T...

Frameworks mean nothing, until they change what gets built! In this article we discuss how Git-Ape turns architecture and governance into delivery controls on Azure because, if frameworks do not shape delivery decisions, they are just decoration. Cloud teams do not have a framework problem. They have an execution problem. The industry is...

Legacy codebases frequently contain hardcoded logic and complex build scripts that depend on specific filesystem structures, making them notoriously difficult to modernize in isolated environments. Docker Sandbox addresses this challenge through a bidirectional workspace sync that preserves the same absolute paths inside the sandbox as on the host....

Practices, Principles, and Strategic Direction Software delivery has entered a new phase. AI agents are no longer confined to autocomplete suggestions in the editor. They are opening pull requests, generating code across multiple files, proposing infrastructure changes, responding to issues with working implementations, and executing multi-step en...

In Part 1 of this blog series set the stage for why platform engineering is being reshaped by agentic AI. (read it here) Basically we outline how instead of humans translating intent through layers of CLIs, SDKs, and bespoke workflows, capable agents can interpret natural-language goals and turn them into safe, validated platform actions using w...

We've talked about the human scale problem and what happens when infrastructure scales but understanding doesn't. If you've been following along, you know the thesis: our tools have outpaced our ability to operate them, and platform engineering is how we're fighting back. But here's the thing - we've been fighting with one hand tied behind our b...

We all know this, even if we don't like to admit it: modern infrastructure can scale infinitely, but human understanding doesn't. We've all seen it happen - organizations going from managing dozens of servers to thousands of containers, from deploying weekly to deploying hundreds of times per day, from serving thousands of users to millions. The...

We keep doing this thing where we solve a problem, celebrate the victory, then realize we've created three new problems we didn't even know existed. Remember when manually configuring servers was the bottleneck? So we built containers. Great! Now we're orchestrating thousands of them. Remember when monolithic deployments were too slow? So we bui...

For the last decade, platform engineering has relied on explicit API interaction layers: CLIs, SDKs, pipelines, wrappers, and UI workflows that translate human intent into machine‑safe API calls. AI agents are now short‑circuiting much of that stack. By combining natural language understanding, reasoning, and direct access to API specifications and...