How much uninterrupted time do engineers actually have to improve the engineering system itself?

Engineers don’t trust AI equally. Learn why trust varies across software development workflows, what drives confidence in AI-generated code, and how trust directly affects software delivery performance.

What if the best use of AI in software engineering isn’t writing code? Discover why debugging, research, and understanding emerged as the highest-value AI workflows in our engineering study.

AI doesn’t fail randomly. It fails predictably. Discover the patterns 250 engineers identified and learn where AI creates value, where it creates friction, and why understanding remains the biggest bottleneck in software development.

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.

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.

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.

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?

Learn how DevEx survey questions uncover specification clarity issues, reduce rework, and improve delivery using real developer feedback and DORA insights.

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.