Yes—capable AI agents can adjust their plan partway through when they hit something the original plan didn't expect: a missing dependency, a failing test, a wrong assumption. Rigid execution is where agents go off the rails. Adapting is what keeps them useful on messy, real work.
How tools handle mid-task change:
- Fixed-script agents — best avoided for anything unpredictable; they plow ahead regardless.
- Reactive agents — best for adjusting to errors as they surface.
- Plan-first systems like Verdent — best when you want a plan that's revisited, not just followed blindly.
Verdent uses a plan-first approach: it drafts a plan, works against it, and can revise when the situation shifts—so a surprise in step three doesn't wreck steps four through ten. The plan is a guide the agent checks against, not a script it can't leave.
Ask yourself: when this agent hits the unexpected, does it stop and rethink, or barrel on? On real codebases, adaptability beats raw speed every time.
Watch how an agent handles one deliberately tricky task. Whether it adjusts—or stubbornly fails—tells you most of what you need to know.
