Agent Loop Cost Simulator

Simulate how costs spiral in a ReAct / tool-use agent loop

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Agent loop cost simulator

A ReAct-style agent runs a loop: think, act, observe, repeat. Each pass appends to a growing scratchpad that gets re-sent on the next call, so cost climbs with every iteration. A task that usually finishes in three loops can quietly run to twenty when the agent gets stuck. This simulator shows the typical cost, the worst-case cost at your max_iterations cap, and helps you set a safe ceiling.

How it works

The simulator models cost as accumulating across iterations, where each iteration’s input grows as the scratchpad lengthens. To keep it transparent it uses a per-iteration token figure that already reflects the average growing context:

typical_cost  = avg_iterations × tokens_per_iteration / 1M × price
worst_cost    = max_iterations × tokens_per_iteration / 1M × price

The gap between the two is your exposure — the amount a single runaway task can cost beyond what you expect.

Tips and notes

  • Always set max_iterations. An uncapped loop is an uncapped bill. Treat the cap as a non-negotiable safety control.
  • Fail loud, fail cheap. Configure the agent to return a clear error when it hits the cap rather than silently retrying the whole task.
  • Log iteration counts. If your average creeps toward the cap over time, your prompts or tools are getting harder to follow — investigate before the bill grows.
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