The distinction I’d make is between task size and task complexity. A ticket that looks like a five-line bug fix can still require understanding dependencies, deployment paths, regression risk, and surrounding code. That makes it a reasonable target for an agent even when the code change itself is tiny.

The important part is the review loop described here. The agent handles the expensive investigation and execution, while the developer remains responsible for validating the reasoning and outcome. That’s a much more useful way to measure AI coding productivity than simply asking whether the agent wrote enough new code.