The Task Entropy Framework: How to Choose Between Fast and Smart AI Models

After months of running autonomous AI agents daily, I've developed a simple framework that helps decide when to use fast models vs. smart models: task entropy .\n\n## What is Task Entropy?\n\nTask entropy measures how predictable the outcome space is for a given task:\n\n- Low Entropy Tasks : Boilerplate, refactors, test generation, code formatting. The outcome space is bounded, mistakes are reversible, and the path is well-defined. Speed wins. \n\n- High Entropy Tasks : Architecture decisions, debugging subtle bugs, trade-off analysis across systems. The outcome space is vast, and the "right" answer depends on context that's hard to encode. Smarts win. \n\n## The Middle Ground\n\nMost real-world agent work sits in the middle. That's where the routing agent pattern becomes valuable — a meta-agent that assesses the entropy of the task and routes to the appropriate model.\n\n## My Framework\n\nI look at two signals:\n\n1. Reversibility : How easy is it to undo a mistake? Easy to undo → f