From velocity
to trust.
Autonomous software delivery is here. The hard part isn't speed anymore — it's accountability. This is how DevBench builds enterprise trust into every step, and how Matthew Dresden helps you adopt it.
Last year we chased velocity.
That problem is largely solved.
When executing a backlog becomes trivial, two questions decide who wins: what should we build, and can we trust the system that builds it?
Coordination overhead, handoffs, and inconsistent quality — speed bought with headcount.
A person defines the work. A disciplined team of single-purpose agents executes it — with gates a human team would recognize.
Garbage in, garbage out — engineered out.
The hardest part of autonomous delivery isn't the agents. It's giving them work so unambiguous it can't be misread. DevBench makes that the easy part — with guided skills.
How a single task gets done.
Trust isn't a promise. It's the architecture.
Six design choices, working together — the answer to "why would I let AI touch production?"
You see everything. You babysit nothing.
A real, completed DevBench run — the public kanon project.
1 declined · 0 blocked at finish
DevBench classifies every block. Most recover automatically; only genuine operator-action blocks notify a human — and it keeps working other tasks while you resolve those later. One block never stalls the run. And it's fully configurable: be as hands-off or as hands-on as you want.
It's backlog-driven — so it isn't only for code.
Whatever you can express as a backlog with clear acceptance criteria, it can execute and prove.
We don't just have the tools.
We help you build the muscle.
Adopting AI-native delivery is a change in workflow and trust, not just tooling — codebase-aware definitions of done, new review practices, and a "trust, but verify" posture. That's the engagement.
AI-SDLC Workshop
Bring a real idea. Over a couple of days we run it through the process and train your team on it.
Guided engagement
We stand the practice up inside your org — with the gates, audit, and judgment model that make it safe.
Your team owns it
You keep the architects and the judgment. The execution layer scales to whatever the backlog demands.