Putting AI Agent Teams to the Test: From Toy Projects to Real-World Tasks

I’ve been setting up AI Agent Teams for a while now, mostly with a mix of curiosity and skepticism. One of those is ‘Team Mustang,’ a team dedicated to supporting my personal software development project. Up until last week, I was just warming up with some light toy projects and defining the roles for each agent. But today, while working on a company task, a specific side-need popped up in my head. After some deliberation, I decided to see if this team could actually handle it.

I quickly defined the requirements through a Discord channel and sent them over to Roy, the leader of Team Mustang. From there, he clarified a few details with me and immediately jumped into the development and verification process alongside Hawkeye and Breda. Sure, I had to click ‘Proceed’ a few times here and there, but other than that, they basically handled everything on their own.

It took only 30 minutes to get the first version ready. Since there was some external integration involved, I stepped in to support that part, pointed out a few bugs that cropped up during the actual integration, and defined some additional requirements to polish the clunky bits I noticed immediately after it started working. From start to finish, the whole process took about an hour and a half.

In the past, this would have taken days—including the time spent hunting for dependency packages and verifying everything. Even if I’d focused on it exclusively, it would have easily eaten up a full day. Since I built this for work, I won’t be able to do the final verification until I’m back in the office on Monday, but based on how it’s behaving so far, I’m pretty confident it’ll work.

For reference, the current agent setup is very lightweight. The role distribution is almost identical to the ‘Planner, Generator, Evaluator’ pattern mentioned by Claude. I found that the moment that the requirement to avoid self-verification bias is introduced, this structure becomes the absolute minimum viable setup. I’ve actually documented the difference in outcomes between using this structure and using a single agent during my initial toy projects; I’ll organize those notes and post them soon.