AI agents consume 10 to 30 times more tokens than a single direct prompt for the same task due to their iterative reasoning loop.

An AI agent doesn't just take one input and produce one output. Instead, it operates in a continuous cycle: it plans what to do, acts on that plan (often by using tools), observes the results, and then revises its approach based on what it learned. Each time it loops through these steps, it must feed the entire history of its interactions—past plans, actions, observations, and even intermediate results—back into the language model as context. This means the model isn't just processing the current request; it's rereading everything the agent has done so far, over and over. As one practical guide explains, this turns a simple request into a series of model calls, where each call carries the full weight of the agent's growing context, multiplying token usage far beyond what a straightforward chat would require.