Stop Agents from Burning Your API Budget Overnight

A coding agent hit an ambiguous error. It retried with an expanding context window. Each retry cost more than the last. By the time someone checked the dashboard the next morning, the agent had looped 240 times and spent $4,200.

The model pricing was exactly right. The call volume was not.

Why existing controls didn't help

Provider spending caps are typically monthly and org-wide. They don't distinguish between your production agent and your staging test. By the time the monthly cap kicks in, the damage is done — and it blocks every other agent on the account too.

Rate limits control how fast, not how much. The agent stayed within its requests-per-second limit. It was making perfectly well-formed API calls. Just 240 of them.

Observability dashboards showed the spike — the next morning. The cost graph was a vertical line at 2 AM. Useful for the post-mortem. Useless for prevention.

How Cycles fixes it

from runcycles import cycles

@cycles(estimate=2_000_000, action_kind="llm.completion", action_name="gpt-4o")
def call_llm(prompt: str) -> str:
    return openai.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
    ).choices[0].message.content

That's it. Before every LLM call, the @cycles decorator reserves budget. If the budget is exhausted, BudgetExceededError is raised and the model is never called. No tokens consumed. No cost incurred.

The same agent with a $15 per-run budget stops after 8 iterations and surfaces the problem immediately: "Budget exhausted. This task needs human review."

What happens now

• Budget checked before every call. The agent can't overspend — the reservation is denied before the API call executes.
• Graceful degradation, not a crash. The agent can catch BudgetExceededError and wind down: summarize progress, switch to a cheaper model, or queue the task for later.
• Per-run isolation. Each agent run has its own budget. A runaway in run #47 can't affect run #48 or another customer's allocation.
• You find out at $15, not $4,200. The budget limit surfaces the problem immediately instead of letting it compound overnight.

The math

	Without Cycles	With Cycles ($15/run cap)
Agent loops	240	8
Cost	$4,200	$15
Time to detect	Next morning	Immediately
Impact on other agents	All blocked by provider cap	None — per-run isolation
Recovery action	Post-mortem and budget reset	Fix the prompt

Go deeper

• End-to-End Tutorial — zero to budget-guarded LLM call in 10 minutes
• Cost Estimation Cheat Sheet — how much to reserve per model
• Degradation Paths — what to do when budget runs out
• 5 Failures Budget Controls Would Prevent — more incidents with dollar math
• Why Rate Limits Are Not Enough — the deeper argument