Foreword

Dear reader,

Most of the conversation about AI in software development focuses on individual coders. A solo developer with the right setup is dramatically more productive than they were two years ago. But serious software gets built by teams, and almost nobody is talking about how teams should work now.

When writing code was the slow and expensive part of software development, stories could be written at a low level of fidelity, and reviews could wait. Most of the detailed requirements were finalized by the person writing the code. Agents have transformed writing code from the slow part to the fast part. Now a lack of upfront definition turns into a week’s worth of bad code, implemented in one afternoon.

As leaders of a custom software consultancy whose teams have shipped more than 300 products over the past 25 years, we see the need for team working patterns to change. Everyone is chasing speed right now. The counterintuitive truth is that for teams, the way to sustain speed is to invest more time in collaboration. If you structure the work so people stay in sync, long-term speed is the byproduct.

Agentic Engineering for Teams is how we structure team collaboration. It preserves what agile got right: short feedback loops, working software as the measure of progress, and teams that adapt to what they learn. It adds the requirements definition rigor that agent-assisted development demands. We don't name specific tools, because tools change. We describe the collaboration structure that makes teams effective when using agentic development tools.

We're publishing this instead of holding it close for two reasons:

1. Most software teams are about to hit the same AI productivity wall we did, and we want to help them move beyond it.
2. This will get sharper when people we've never met read it and suggest enhancements. Email us with your feedback.

If you lead a software team, remember this: the AI productivity everyone is chasing is best measured by how well your team works together.