Engineering Workflow

My use of coding agents gradually became work on the environment around them: current context, tool access, focused checks, review and recovery. The useful distinction is not how much an agent can do, but what makes its result defensible.

I built a personal AI reviewer for GitHub Pull Requests, not as a cold rule bot, but as a local and observable tool that checks known risk patterns and turns them into useful engineering conversation.

I arrived at Liberty with a working model for agentic development. Now the company is exploring a more formal approach built around reusable agent skills. I don't know which model will prove better yet, and that uncertainty is the interesting part.