If you always let the AI drive, you might forget how.
Skill atrophy happens when you stop practicing a skill and gradually lose the ability to perform it. Pilots who rely too heavily on autopilot find their manual flying skills degrade. Surgeons who don't operate regularly lose precision. The same risk exists for developers who let AI write all their code.
If an AI agent handles your error handling, debugging, architecture decisions, and algorithm implementation, you might find that when the AI is unavailable, you're slower and less confident than you used to be. Not because you were never capable - but because skills need regular exercise to stay sharp.
This isn't a reason to avoid AI tools. It's a reason to use them thoughtfully, maintaining the skills that matter while letting AI handle the parts where human effort adds the least value.
Agentic engineering explicitly requires strong engineering fundamentals. You can't effectively review AI-generated code if you don't deeply understand the code yourself. You can't write good specs without understanding the technical constraints. You can't identify when an agent's architectural decision is flawed if you've never designed a system yourself.
The paradox: the better AI gets at writing code, the more important it becomes for engineers to understand code deeply - because the human's role shifts from writing to evaluation, and evaluation requires even stronger skills than writing.