How to Prevent OpenAI Agents from Getting Stuck in Costly Loops

In this blog post How to Prevent OpenAI Agents from Getting Stuck in Costly Loops we will explain why AI agents repeat themselves, how those loops increase costs, and the controls that stop them before they affect your budget or operations.

An OpenAI agent is more than a chatbot. It can assess a request, choose a tool, review the result and decide what to do next. That might involve searching company records, updating a customer relationship management system, preparing a report or asking another specialised agent for help.

The risk is that an agent may not recognise when it is making no progress. It can keep retrying the same failed action, passing work between agents or rewriting an answer that will never meet an unclear goal. Each step may consume paid AI processing, call another paid service and create extra load on your systems.

Why OpenAI agents get stuck

OpenAI’s Agents SDK provides a runner that manages the workflow. In plain English, the runner asks the AI model what to do, executes any requested tools, returns the results to the model and repeats the process until the agent produces a final answer.

This loop is useful because it allows an agent to complete multi-step work. However, the agent needs a clear finish line. Without one, it may continue because it believes one more search, retry or handoff will solve the problem.

Common causes include:

• Unclear completion criteria. The agent has not been told exactly what a successful result looks like.
• Poor tool feedback. A connected system returns “failed” without explaining whether the problem is temporary, permanent or caused by bad input.
• Unlimited retries. The workflow keeps retrying an unavailable service without a sensible stopping point.
• Conflicting instructions. One instruction says to complete the task at all costs, while another prevents the required action.
• Agent handoff loops. Two specialist agents repeatedly send the task back to each other.

These problems are different from a normal service outage. Our guide on building AI agents that recover from failure explains how to save progress and resume work. Here, the focus is deciding when the agent should stop rather than recover.

1. Set a hard limit on agent turns

A turn is one cycle in which the model reviews the available information and decides what to do next. The OpenAI Agents SDK allows developers to set a maximum number of turns for a run.

This is your emergency brake. A simple customer lookup may need three or four turns, while a research task may need more. The correct limit should be based on the task, not one generous setting applied to every agent.

from agents import Agent, Runner
from agents.exceptions import MaxTurnsExceeded

agent = Agent(
 name="Customer Support Agent",
 instructions=(
 "Resolve the request using the approved tools. "
 "If the required information is unavailable, stop and explain why."
 ),
 tools=[lookup_customer, check_order]
)

try:
 result = Runner.run_sync(
 agent,
 "Check the delivery status for order 4821",
 max_turns=6
 )
 print(result.final_output)
except MaxTurnsExceeded:
 send_for_human_review("Order 4821 exceeded six agent turns")

The important business decision is what happens after the limit is reached. Do not simply restart the task, because that can create a larger loop. Record the reason, preserve completed work and send the exception to a person or a controlled review queue.

2. Limit tool calls as well as turns

A turn limit alone is not enough because an agent may request several tools during one turn. You should also cap calls to expensive or sensitive services, such as web searches, document processing, financial systems and external data providers.

Set separate limits according to risk. For example, an agent might be allowed ten read-only searches but only one attempt to update a customer record. Actions involving payments, account changes or data deletion should normally require human approval.

Tool guardrails are checks applied before or after an agent uses a connected system. They can reject invalid requests, block duplicate actions and prevent results from being accepted when required information is missing. This extends the ideas covered in our article on building guardrails in the OpenAI Agent SDK.

3. Detect when the agent is making no progress

A well-designed workflow does not only count activity. It checks whether that activity is producing a different or better result.

Your monitoring should flag patterns such as:

• The same tool being called repeatedly with identical details.
• The same error appearing more than twice.
• Two agents handing the task back and forth.
• The agent producing nearly identical answers after each review.
• No new data being added after several steps.

When one of these conditions appears, the agent should stop with a useful status such as “customer number is invalid” or “finance system remains unavailable.” That gives an employee something actionable instead of a vague failure message.

For multi-agent workflows, ownership must also be explicit. Our guide to choosing handoffs or agents as tools safely explains how workflow structure affects accountability, context and cost.

4. Use retry rules that match the failure

Not every error deserves a retry. A temporary timeout may clear after a short wait, but an invalid customer number will remain invalid no matter how many times the agent submits it.

Classify errors into three practical groups:

1. Temporary errors can be retried two or three times with a longer delay between attempts.
2. Permanent errors should stop immediately and request corrected information.
3. High-risk uncertainty should pause for human review rather than allowing the agent to guess.

This reduces cost while improving reliability. It also avoids the dangerous situation where an agent interprets repeated failure as permission to try increasingly creative actions.

5. Give every task a financial budget

Monthly platform limits are useful, but they act too late if one faulty workflow consumes a large share of the budget. Set limits at the project, agent, customer and individual task levels.

Monitor the number of model requests, input and output volume, tool charges, total run time and estimated cost. The Agents SDK can aggregate usage across model calls, tool activity and handoffs, while tracing provides a step-by-step record of what happened during a run.

Consider a 200-person services company processing 1,000 routine requests each working day. If a faulty agent wastes just 30 cents per request through repeated searches and retries, that is roughly $6,600 a month in avoidable processing costs. The larger loss may be the staff time spent investigating inconsistent results.

6. Keep context controlled

Context is the information supplied to the AI model so it can understand the task. If every retry includes the full conversation, large documents and previous tool results, each repeated step can become more expensive than the last.

Store task progress separately, remove duplicated results and pass only the information needed for the next decision. Our explanation of multi-turn state in OpenAI agents covers how to preserve continuity without repeatedly sending unnecessary data.

A practical pre-production checklist

• Define what success, failure and partial completion mean.
• Set maximum turns, tool calls, retries, run time and cost.
• Block duplicate or high-risk actions.
• Test unavailable systems, bad data and conflicting instructions.
• Send stopped tasks to a named owner with a clear explanation.
• Review traces and costs before expanding the workflow.

The goal is not to prevent agents from working independently. It is to give them the same boundaries you would give a capable employee: a clear objective, approved tools, a spending limit and a point where they must ask for help.

CloudProInc combines more than 20 years of enterprise IT experience with hands-on knowledge of OpenAI, Azure and Microsoft security. As a Melbourne-based Microsoft Partner and Wiz Security Integrator, we help organisations build AI workflows that are useful, measurable and controlled rather than expensive experiments hidden inside the IT budget.

If you are not sure whether your OpenAI agents can detect a stalled task or control their own costs, we are happy to review the workflow and identify the practical guardrails it needs — no strings attached.