For a long time, “using GitHub Copilot” meant typing a prompt in VS Code. That was the Copilot we knew: the one embedded in the editor, the one that completed code, the one that felt like a natural...

Coding agents have reached a point where they can perform meaningful, multi‑step work across an entire codebase. They can refactor dozens of files, rewrite architectural boundaries, introduce or re...

Deterministic vs Non-Deterministic Work GenAI is extraordinary at handling non‑deterministic workflows: the kind of work where ambiguity exists, where interpretation matters, and where the “right”...

Coding agents were fun when they felt like magic. You typed a request, something happened behind the curtain, and results appeared. Then usage‑based billing became the norm. The curtain stopped bei...

We have spent the last few months watching coding agents evolve from clever autocomplete to something closer to a real engineering component. We now package them, version them, sign them, distribut...

The problem: one agent doing everything Picture a typical Copilot session. You ask it to add a feature. The agent searches your codebase, reads files, makes edits, runs tests, hits an error, backt...

Building agent configuration is the easy part. Keeping it consistent is not. You write a useful set of instructions. You craft a skill that encodes how your team handles a specific workflow. It wo...

Most of us began our generative AI journey the same way: Write a prompt, get a code snippet, paste it into the project, adjust until it works. It is useful, but it barely taps into what AI ca...

Code agents are changing how we build software. No longer by simply producing isolated snippets or filling in boilerplate, but by participating directly in the development workflow. They read entir...

When people first use code agents, the experience feels magical. You ask for a feature, the agent scans your code, edits files, runs tests, and keeps going. Then reality shows up: context grows, t...