Why controlling AI reasoning effort matters as much as choosing the right model

A software engineer argues that selecting an AI model tier is only half the cost equation when running autonomous agents — the other half is controlling how much reasoning effort that model is allowed to spend per task. Just as upgrading a motorcycle to Stage 3 raises fuel consumption on every ride regardless of need, setting reasoning effort to maximum on trivial tasks like renaming a variable or formatting an import wastes compute budget unnecessarily. The author outlines three signals — verifiability, ambiguity, and risk — to help classify whether a task warrants high or low reasoning effort before spawning an agent. In multi-agent fan-out scenarios, the cost impact compounds, since effort multiplies across every spawned instance. The piece frames effort as an auditable engineering policy, orthogonal to model routing, that should be defined before execution rather than left at a default maximum.