Every new piece of software has bugs. The question is who finds them first: you, or your users. Before a new order-management application went live, we wanted it tested the way staff would really use it: busy people entering orders, recording payments and, at times, editing the same record at once.
The usual answer is a test plan and a week of people clicking through screens. Instead, six AI testers worked at the same time. Each received a private copy of the application and its data, a defined part of the job and one instruction above all: only report what you can prove.
What they found
Round one reported 64 findings, merged into 45 distinct bugs. The most serious were the kinds of failures that are hard to see in a demonstration: one person could quietly overwrite another’s change; a double-click could save twice; and certain totals, dates and money fields could be wrong.
All 45 were fixed and released. Round two used fresh copies, a list of resolved issues and new areas to explore. One tester re-checked every fix; the others reached corners not touched in round one, including archiving, deleted records, payments covering several orders, backup and restore. That round found, fixed and released another 48 issues.
Every issue came with exact reproduction steps. The second round mattered as much as the first: it checked that a fix held, rather than simply trusting that it did.
Why this is useful beyond the engineering team
The bugs that hurt a business are rarely the ones a short demonstration shows. They are the quiet failures: a colleague’s change silently lost, a total slightly wrong, or a screen saying something is saved when it is not. They surface later, in front of a customer, and they cost trust.
AI agents are useful for this thorough, repetitive work because they can investigate several carefully defined paths in parallel and document each finding step by step for a developer to reproduce and fix.