Why AI Agents Get Stuck in Infinite Loops (and How to Kill Them)
Stop the $3,000 overnight surprise bill before the second retry.
AI agents loop because standard retry logic treats unexpected errors by rewriting prompts and re-executing tools indefinitely. Exogram stops runaway loops by enforcing hard per-task call budgets and instant circuit breakers that cut execution cold after three failed attempts.
What Actually Happens in Production
A developer left an autonomous agent running overnight to scrape competitor pricing. Around 1:30 AM, the target website returned an HTTP 429 rate limit error. Instead of stopping, the agent reasoned that the request failed due to bad formatting. It generated a new prompt, retried, hit another 429, and tried again. Over six hours, it attempted 14,000 tool calls and burned $2,400 in API credits on an empty result.
Why Writing “Please Don't Do This” in Your Prompt Fails
Writing "stop if you see an error" in your system prompt fails because language models are built to keep the conversation going. When an error occurs, the model treats the error message as fresh input to solve. You cannot use English words to stop a program from retrying—you need external code that counts the attempts and pulls the plug.
The Fix: Putting a Real Lock on the Door
Exogram places a hard circuit breaker outside the LLM context window. Every time your agent requests a tool call, Exogram checks the task counter. If the agent exceeds your configured budget (for example, 5 calls or $0.50 per task), Exogram blocks the execution in 0.07ms and returns a hard stop signal.
from exogram import Exogram
exo = Exogram(api_key="your_key")
# Wrap your agent tool execution with a hard circuit breaker
@exo.guard(max_calls_per_task=5, spend_limit_usd=1.00)
def call_external_api(endpoint, payload):
return requests.post(endpoint, json=payload)Frequently Asked Questions
Why do AI agents retry so aggressively when an API fails?
Most agent frameworks (like LangChain or AutoGen) are designed with self-healing loops. If an output doesn't match expected JSON or returns an HTTP error, the framework automatically asks the LLM to fix the error and try again, creating an infinite spending loop.
How does Exogram detect a loop before it burns my wallet?
Exogram tracks call counts, payload hashes, and execution velocity in a local SQLite ledger. If an agent calls the same tool repeatedly with similar payloads, Exogram trips the circuit breaker instantly.
Will Exogram slow down my agent while checking for loops?
No. Exogram checks the counter locally in 0.07 milliseconds before the network request is initiated. There is zero perceptible lag.
Lock down your bots in 5 minutes
Give your AI agents freedom to do real work without the fear they will break your software or empty your wallet.