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 → fast model. Hard to undo → smart model.\n2. Blast Radius: How many files/systems does this affect? 1 file → fast. 10,000 files → smart.\n\n## The Sweet Spot\n\nThe ideal setup isn't choosing one model — it's fast switching between them based on task requirements. Autocomplete? Fast. Architecture decisions? Smart.\n\nWhat framework do you use for model selection in your agent workflows?