I've never used breakpoint debugging, was always a printf debugger. And now an agent can do that loop for me.

Prompt is usually something along the lines of:

>I would expect the behavior of this to be [X] - instead I'm observing [Y]

And the agent will form hypothesis, place printf statements, compile, and scrape logs on loop - each loop ruling out hypothesis or narrowing down what portion of the code is responsible for the unexpected behavior.

It has been able to pin-point the exact line(s) of code responsible every time I've reached for it so far.

From what I've seen they're more likely to run a python -c "import your_code; your_code.do_stuff()" experiment to figure out what's going on though.

A key feature of AI coding assistants and coding agents is troubleshooting. It turns out that LLMs excel at pattern matching, specially when coupled with feedback signals. It turns out that troubleshooting represents just that. A few years ago people searched the likes of stack overflow to fix problems, and it turns out LLMs can do the equivalent of that much faster.

Tests stick around and prevent future problems, whereas the debugger only shows me something once.