How to Route AI Coding Tasks by Risk to Cut Costs and Improve Reliability

A software developer has proposed a task-routing framework for AI coding tools that assigns work to free or paid models based on the potential cost of failure rather than model quality alone. The system uses three tiers — low, medium, and high risk — where only tasks with silent, hard-to-detect failure modes are sent to the strongest available model by default. A lightweight shell script acts as an objective acceptance gate, running type checks, existing tests, and a file-scope diff to validate each AI-generated change before it is accepted. Every task is logged in a single line of JSON, allowing developers to audit routing decisions weekly and measure how often free-tier models pass without needing escalation. The approach aims to replace guesswork with measurable data, helping teams build genuine intuition about where expensive AI models are truly necessary.