Your AI agent was supposed to handle that task. You check the logs — it ran for 9 turns, burned $0.12 in token costs, and returned... nothing. No error. No partial answer. Just an empty response.
The agent didn't crash. It didn't time out in a way you'd notice. It retried silently until it hit a turn limit, then gave up and returned null.
This isn't a hypothetical. Mervin Praison ran 10 identical agent tasks with a broken tool. Three of them returned nothing — zero output — after running through their entire turn budget. The model kept trying the same failed approach, over and over, without telling anyone it was stuck.
If you're running AI agents for your small business, this bug is already costing you money. Here's how to catch it.
What the Infinite Retry Loop Looks Like
Here's the pattern:
- Your agent calls a tool (an API, a database lookup, a web search)
- The tool fails — maybe it returns an error, maybe it returns unexpected data
- The model interprets the failure as "try again" rather than "stop and report"
- It retries the same call, same way, same broken parameters
- It hits your configured turn limit (10, 20, 50 turns)
- It returns an empty result — no error, no explanation, no value
The cost: Each retry burns tokens. Each retry burns time. And the worst part? You might not notice for days, because the agent doesn't flag it as an error. It just... finishes silently.
Why This Happens
Most AI agent frameworks don't distinguish between "this failed, try a different approach" and "this failed, try the exact same thing again." The model sees a tool error and its default behavior is to retry — often with the same parameters that caused the failure in the first place.
Common triggers:
- Broken API endpoints — the tool returns an error code the model doesn't understand
- Rate limiting — the model gets a 429 response and retries immediately instead of backing off
- Schema mismatches — the model passes wrong parameters, gets an error, and passes the same wrong parameters again
- Authentication failures — expired tokens that the model can't refresh on its own
The model isn't being dumb. It's being consistent. It has no mechanism to say "I've tried this three times the same way and it keeps failing — I should stop."
How to Detect Silent Retries in Your AI Agents
1. Log Every Turn — Not Just the Final Answer
Most businesses only check the final output of their AI agents. That's like only reading the last page of a report. Turn-level logging shows you:
- How many turns each task consumed
- Whether the agent is repeating the same action
- Where it got stuck
Action: Add turn-level logging to any agent you run. If a task consumes more than 5 turns on a single subtask, flag it for review.
2. Set a Turn Budget Per Tool — Not Just Per Task
Your overall turn limit (say, 20 turns) might seem generous. But if your agent spends 15 of those turns retrying the same broken API call, you've wasted 75% of your budget on one failure.
Action: Set a per-tool retry limit. If the same tool fails 3 times consecutively, force the agent to stop and report the failure rather than continuing to retry.
3. Add a "Stuck Detection" Check
After each tool call, check:
- Is the output the same as the last attempt? → Stuck
- Is the error message identical? → Stuck
- Has this tool been called more than 3 times in this task? → Likely stuck
Action: Build a simple stuck-detection rule into your agent pipeline. If the agent appears stuck, interrupt it and return a structured error message instead of letting it burn turns.
4. Monitor Your Token Spend Per Task
If your average task costs $0.02 and suddenly one task costs $0.14, that's a signal. Token cost anomalies are the canary in the coal mine for infinite retries.
Action: Set up a cost threshold alert. If any single task exceeds 3x your average cost, flag it for investigation.
5. Require Explicit Failure Reporting
Configure your agents so that when they hit a turn limit, they don't return empty — they return a structured failure report:
{
"status": "failed",
"reason": "turn_limit_reached",
"turns_used": 20,
"last_action": "api_call_to_X",
"last_error": "HTTP 429 Rate Limited"
}
Action: Every agent should have a "maximum effort" boundary that returns a diagnostic payload, not silence.
The Real Business Impact
Let's put numbers on this. Say you're running a customer service agent that handles 100 conversations per day:
- Normal cost: $0.03 per conversation = $3/day
- With 10% stuck in retry loops: 10 conversations × $0.15 (5x normal cost) + 90 × $0.03 = $1.50 + $2.70 = $4.20/day
- Monthly difference: $4.20 × 30 = $126 vs $90 — a 40% cost increase from bugs you can't see
And that's just the direct cost. The opportunity cost — customers who got no response, leads that went cold, tasks that never completed — is likely 10x the token cost.
Quick-Start Checklist
- [ ] Enable turn-level logging on all AI agents
- [ ] Set per-tool retry limits (3 retries max)
- [ ] Add stuck-detection rules (same action, same error = stop)
- [ ] Configure cost anomaly alerts (3x average = investigate)
- [ ] Require structured failure reports instead of empty returns
- [ ] Review agent logs weekly for tasks that hit turn limits
The Bottom Line
AI agents that fail silently by retrying forever aren't a theoretical problem — they're a budget leak that most small businesses never detect. The fix isn't complicated: add limits, add logging, add failure reporting. But you have to know the bug exists before you can fix it.
Now you know. Check your agents tonight.
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