Agile Otter Blog

The Faros report on AI-assisted development has been rattling around in my head for a few days. The story it tells is a strange one: Teams are producing more code.  More tasks are being completed.  More pull requests are being created.  And yet : waiting times are up,  review times are up,  lead times are up,  incidents are up, and  bugs are up.  The picture shows a development process that's getting busier without getting faster. My first reaction was the same as everyone else's. Maybe the AI-generated code just isn't very good. The report certainly contains evidence that quality is suffering. But the more I looked at the numbers, the more I focused on waiting times. I've spent enough years looking at value stream maps to have a habit of looking for queues. A surprising amount of "developer behaviour" turns out to be queue behaviour in disguise. A review problem turns out to be a queue. An approval problem turns out to be a queue. A testing probl...

 I've written repeatedly about the 7 Code Virtues, and they have been reliable guides for me, as well as a language for describing goodness in code. When I started applying this agentically, I realised that I may have been wrong about ordering (as I suspected all along) and adjusted to 'Working is prime, the rest are equal', and this has served me well. The agentic code still wasn't going as well as I hoped. After a bunch of reflection, ChatGPT, and trial and error, I discovered that I was missing Coherence.  Most simply described, coherence is the degree to which concepts, abstractions, vocabulary, architecture, patterns, and representations of the system reinforce one another. Over its evolution, a coherent system increasingly feels like a single language. This goes hand in hand with the 'developed' code virtue. Coherent systems increase the value of learning because knowledge gained in one part of the system becomes useful elsewhere. This raises the bar for ...