AI Agents Are Measured by What They Ship, Not Just Token Counts
AI coding agents are changing how developers should think about performance and cost. Unlike a traditional chatbot, an agent rarely answers a programming request in a single step. It may break the task into a plan, generate code, inspect the project, run tests, interpret failures, revise its work, and repeat the cycle until the result is usable. That workflow can consume 10 to 50 times more tokens than a basic chat prompt. Yet token volume alone does not show whether the system is delivering value. As agents become more capable, they also make more model calls, turning raw usage metrics into an increasingly incomplete measure of productivity. The more meaningful question is what the agent actually ships: working code, reliable fixes, and completed tasks. For developers and companies evaluating AI tools, output quality, success rates, iteration time, and total engineering impact may matter far more than simply counting tokens.