An AI agent self-corrects code by comparing its output against feedback, then changing the code until the feedback improves. The feedback can come from tests, type checks, lint errors, build logs, runtime errors, or human review comments.
The process is usually simple: generate a change, run a check, inspect the failure, revise the implementation, then run the check again. This can work well for syntax errors, failing unit tests, missing imports, and narrow regressions. It works less well when the requirement itself is wrong or incomplete.
Verdent's Plan Mode and review workflow help reduce that risk. Planning clarifies the goal before code changes begin, and workspace isolation keeps corrections from damaging unrelated work. The strongest agentic coding workflow is not "AI fixes itself forever." It is bounded self-correction plus tests, diffs, and human approval before merging.
