My experience so far is that, if you're limiting the "capacity" to junior engineer, yes, especially when it's seen a problem before. It's able to quickly realize a solution and confirm the solution works.

It does not works so well for any problems it has not seen before. At that point you need to explain the problem, and instruct the solution. So a that point, you're just acting as a mentor instead of using your capacity to just implement the solution yourself.

My whole team has really bought into the "claude-code" way of doing side tasks that have been on the backlog for years, think like simple refactors, or secondary analytic systems. Basically any well-trodden path that is mostly constrained by time that none of us are given, are perfect for these agents right now.

Personally I'm enjoying the ability to highlight a section of code and ask the LLM to explain this to me like I'm 5, or look for any potential race conditions. For those archiac, fragile monolithic blocks of code that stick around long after the original engineers have left, it's magical to use the LLM to wrap my head around that.

I haven't found it can write these things any better though, and that is the key here. It's not very good at creating new things that aren't commonly seen. It also has a code style that is quite different than what already exists. So when it does inject code, often times it has to be rewritten to fit the style around it. Already, I'm hearing whispers of people say things like "code written for the AI to read." That's where my eyes roll because the payoff for the extra mental bandwidth doesn't seem worth it right now.