AI Agent Token Costs: A Practical Checklist for Developers Before Your Demo Becomes Expensive AI agents are fun when they are running in a demo. They become less fun when every task sends a huge conversation history, five tool results, three retries, and a giant system prompt into the model. The problem is not just money. Token waste also creates slower responses, noisier outputs, harder debugging, and more unpredictable behavior. After looking at current DEV discussions around AI agents, production AI systems, gateway platforms, and prompt engineering, one pattern is clear: developers are moving beyond “write a better prompt” and asking a better question: How do I make this AI workflow repeatable, observable, and affordable? This checklist is for that moment. If you are building an AI coding assistant, internal support agent, research bot, sales assistant, or automation workflow, use this before scaling usage. 1.…