Agents

Two posts back, I used Kent Beck’s 3X framework to argue that agentic engineering gets read at different phases of maturity depending on how far you sit from the code. Last post, I argued that more code isn’t enough. Agents compress execution to nearly free, but judgment is still as critical as ever: deciding what to build, owning intent, understanding the result well enough to steer the next loop.

In my last post using Kent Beck’s 3X Framework, I argued that agentic engineering is being perceived at different phases of maturity depending on where you sit. Executives believe the systems are mature, in Extract. Practitioners are doing the work of Expand. The practice, the tools, the workflows are still churning in Explore.

Agentic engineering is happening at a breakneck pace, and many are wondering if they are making the “right” decisions and using the “right” approaches. I’ve been a proponent of not chasing the newest fad, but to instead learn about what’s being used across the industry and keeping your options open.

We’ve been building a more agentic engineering organization at League over the last ten months. I’ve been writing blog posts about different aspects along the way. In April, I felt like we’d crossed a threshold.

Most teams measure AI impact on software delivery the same way: before and after comparisons on tasks that were already in the plan. A migration that used to take three weeks now takes two days. A bug that took a week to diagnose gets resolved in an afternoon. These are real wins.

There’s a growing belief in engineering circles that because coding agents can generate code so cheaply and quickly, we no longer need to invest in platform frameworks, services, and shared systems. The argument goes something like: code is essentially free now, so why bother with reusable building blocks? Just let agents generate what you need on the fly.

League (my day job) recently enabled mandatory GitHub Copilot code reviews on all pull requests. Not that long ago, I would not have supported this idea. Models, and the agent systems that use the models, have improved to the point of frequently finding non-trivial issues with code in a pull request.