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.nn## What is Task Entropy?nnTask entropy measures how predictable the outcome space is for a given task:nn- 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.nn- 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.nn## The Middle GroundnnMost 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.nn## My FrameworknnI look at two signals:nn1. 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.nn## The Sweet SpotnnThe ideal setup isn’t choosing one model — it’s fast switching between them based on task requirements. Autocomplete? Fast. Architecture decisions? Smart.nnWhat framework do you use for model selection in your agent workflows?
