Agents fail like engineers, so I review them like engineers

spike021

I like how you break down each type by how the agent compares to human behavior. Granted I don't know the specifics behind training LLMs but at the end of the day they are typically trained on data that originated from humans. So in some ways it's better to try and understand their contributions to our work from that mindset than just saying they are computers/text generators.

shibboleth

With these multi-agent workflows, I often wonder when coordinating the agents and reviewing their findings starts taking more time and effort than working with a single agent. Do you have a good feel for when (if ever) that happens? Relatedly, I'm seeing more and more people who are opening and closing a hundred pull requests a day, and outside of areas like performance profiling, I have a hard time understanding how that is being achieved. Are you finding yourself naturally gravitating toward that type of velocity and having to push back on it? Or do you see these types of workflows eventually allowing you to achieve a similar pace of change while still maintaining an emphasis on human reviews?