This article was originally published on Built In by Olivia McClure, based on an interview with Airwallex Engineering Director Andy Chow.
What’s your rule for fast, safe releases — and what KPI proves it works?
Our rule is simple: Every change requires a merge request with human review. We make no exceptions, even for code by AI agents. We combine this with automated CI gates and progressive rollouts to ensure speed doesn’t compromise safety.
Our internal platform, AirDev, runs AI agents that autonomously implement well-scoped tasks. These agents submit merge requests that follow the same review and CI pipeline as human-authored code.
Throughput of merged changes proved the system works. Since launching AirDev in late 2025, our agents have contributed over 11,000 merged MRs across 200+ repositories. Monthly volume grew from 60 MRs in the first two months to over 3,700 in a single month by March 2026. That’s roughly 120 agent-authored MRs reaching production every day, all passing the same quality gates as human code.
We now ship previously deprioritized work continuously alongside new features. We track deployment frequency and change failure rate to ensure we ship faster without regressions. Our internal survey gave AirDev a 4.2/5 satisfaction rating, with 91 percent rating it four or five stars.
Which standard or metric defines “quality” in your stack?
Our quality principle is simple: AI-generated code must meet the exact same bar as human-written code. Every merge request follows the same path. Whether from an engineer or an AirDev agent, it must pass automated CI checks, static analysis, SonarQube quality gates and human review. If a gate fails, the change doesn’t ship.
“Our quality principle is simple: AI-generated code must meet the exact same bar as human-written code.”
This rigor matters because our AI agents have now contributed over 11,000 merged MRs. At that scale, you can’t afford a separate quality bar. Our engineers review every agent-authored MR to verify it follows existing patterns, handles edge cases and avoids unnecessary complexity.
We’ve learned that context beats instructions. Agents that understand why a change is needed produce better code than those following rigid specs. That insight shapes our quality standard. We want solutions that are idiomatic and easy to maintain.
Finally, we prioritize small, focused changes. We’d rather ship 120 focused MRs a day than a few sprawling ones. Small changes are easier to review, safer to deploy and faster to roll back.
Name one recent AI/automation shipped and its impact on the team or business.
We built AirDev to handle software tasks from start to finish. Our AU agents manage everything from reading a Jira ticket to submitting a merge request. These agents work in isolated environments with access to our full toolchain, including Git, GitLab, Jira, Confluence and CI/CD pipelines. They clone repos, analyze existing patterns, implement changes, and open MRs for human review.
Since launching in late 2025, AirDev has produced over 11,000 merged MRs across 200+ repositories, contributing roughly 440,000 lines of code. Monthly output grew 60 times, jumping from 60 merged MRs at launch to over 3,700 in March 2026.
The impact goes beyond volume. Engineers have reclaimed about 20 engineering-years previously spent on repetitive tasks like configuration updates, boilerplate endpoints and test coverage. This frees them to focus on design and mentoring.
The biggest surprise was the shift in priority. When the cost of a well-scoped task approaches zero, we can finally tackle work we used to ignore. We now fix technical debt alongside new features instead of pushing it off. AI boosts our capacity without lowering our standards.
