AI Coding Agents: Stop Counting Tokens, Start Counting What Ships

When a developer assigns a task to an AI coding agent, the interaction goes far beyond a single query. The agent plans, writes code, executes tests, and iterates to fix mistakes—often consuming 10 to 50 times more tokens than a standard chatbot prompt. This token explosion is not a flaw; it reflects the agent's deeper, multi-step reasoning and execution. As these agents become more capable, they make more internal calls, driving up computational costs. For engineering leaders, the key metric should shift from raw token count to tangible outcomes: features shipped, bugs resolved, and time saved. Understanding this trade-off is critical for budgeting and scaling AI-assisted development. The real question isn't how many tokens an agent burns, but how much working software it delivers.