AI success isn’t determined by model quality alone. Learn how high-performing engineering teams use AI for debugging, implementation, and delivery while avoiding review overload, rework, and trust issues

What Hundreds of Engineers Reveal About the Journey Toward AI-Native Delivery

How do you measure the real impact of AI in software engineering? This case study introduces Agentic Experience (AX), revealing why AI-generated productivity gains often disappear through review, validation, and rework before reaching production.

Learn how Developer Experience surveys help engineering organizations uncover delivery friction, identify root causes, prioritize improvements, and turn developer feedback into measurable delivery outcomes across teams.

Use DevEx AI-Assistance survey insights to identify where AI improves engineering work, where it creates rework and delivery bottlenecks, and how AI impacts software delivery across teams.

Last week at the Infoshare conference, Jon Kern — co-author of the Agile Manifesto — and I opened the Tech Trends stage with a talk about how AI is changing software delivery, engineering organizations, and ultimately the meaning of agility itself.

We can have faster and faster tools, and a slower and slower delivery system. And for quite a while, we may not even notice. Unless we use data that helps us understand how our delivery system really works. But let’s start from the beginning. To simplify: what would delivery look like if it were a snake?

Use DevEx survey insights to identify monitoring gaps. Diagnose alert noise, missed signals, and slow detection before issues impact users and delay response.

Use DevEx survey insights to identify unclear priorities and decision gaps. Diagnose conflicting goals, slow decisions, and wasted time before they impact delivery.

Use DevEx survey insights to identify release bottlenecks. Diagnose slow deployments, manual steps, approval delays, and release friction before they impact delivery speed.

Use DevEx survey insights to identify gaps in test quality. Diagnose missing coverage, unreliable tests, and production bugs before they impact users.

Below is a practical, engineer-facing interpretation of the levels—plus a “taste/time-horizon” perspective from my conversation with Steve, and a set of proven tips & tricks from Addy Osmani’s orchestration patterns and Steve’s own maintainer workflows.

Discover DevEx survey questions that help engineering teams diagnose codebase friction. Learn how to improve code discoverability, shared knowledge, and safe code changes.

AI coding tools are everywhere. Many teams see faster local throughput: more code drafted, more ideas explored, less friction to start.

From Uber’s agent stack to OpenAI’s small teams: the new operating model for engineering

Here are 14 deep dive questions you can ask your developers to uncover the causes of friction in task batching, along with guidance on how to interpret the results, common patterns engineering teams encounter, and practical first steps for improvement.