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.

Large Language Models (LLMs) are powerful tools for generating text, answering questions, and coding. We’ve moved beyond generating content, and LLMs are now being used to take actions as agents — independent entities that can act, use tools, and interact with their environment. You probably already know all of this.

I often wonder if I’m being effective at my job. Might be related to my impostor syndrome, but in engineering management, the signals of effectiveness aren’t always clear. I have some basic, high level criteria I try to think about monthly, or so, to provide some insight.

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.

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.