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Agent Loops Are Burning Your Budget: How to Cap AI Agent Costs

Agents re-send their whole context on every step, so cost grows with every step they take. A runaway loop can cost more in an hour than a normal day.

Quick answer

Cap agent costs with a hard step limit, a per-task token budget, a cached system prompt and tools, and cheaper models for simple steps.

Why agents get expensive

Each step sends the system prompt, the tool definitions and the growing history again. A 20-step task can bill the same prefix 20 times, and tool results pile up in the context.

Six controls that work

  1. Max steps. Stop at a sensible limit and return what you have.
  2. A per-task budget. Track tokens per task and stop or escalate when it's exceeded.
  3. Cache the prefix. System prompt and tools are identical on every step, so they should be cache reads, not full-price input.
  4. Trim tool results. Keep what the next step needs, not whole pages or files.
  5. Cheaper models for simple steps. Planning may need a strong model; formatting and lookups rarely do.
  6. Detect loops. The same tool call with the same arguments three times is a bug, not progress.

Measure cost per successful task

Track what a completed task costs, including failures and retries. Estimate yours with the AI agent cost calculator.

apicuts sets these limits in the gateway and alerts you on spend anomalies. See budgets, limits and alerts.

Frequently asked questions

How much does an AI agent cost per task?

Multiply steps per task by tokens per step and your model's prices, then add retries. Long tasks on large models can cost dollars each; capped and cached ones much less.

How do I stop runaway agent costs?

A hard step limit, a per-task token budget and spend alerts. Together they make a runaway loop a small, visible cost.

Want us to find your 20%?

Book a free 15-minute API cost teardown. Bring last month's provider usage and we'll show you your biggest waste and the first fix we'd ship.

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