AI Agent Loop Detection: How to Identify Runaway Agents

Runaway AI agent loops are the most expensive failure mode in production AI systems. Here is how to detect them before finance notices.

What a Runaway Agent Loop Actually Is

An agentic system receives an instruction with a success condition: complete the task, evaluate the result, and keep going until the spec is satisfied. When that evaluation mechanism fails or the success condition is too hard to meet, the agent loops indefinitely. Each loop is a successful API call. None register as errors. The cost accumulates in silence.

Why Standard Monitoring Cannot See It

APM tools measure latency, error rate, and throughput. None of these signals have any concept of task completion or loop depth. A loop running 80 iterations looks identical to a loop running 2 iterations from APM's perspective — both are sequences of successful requests. The cost difference between them can be 40x, and your monitoring infrastructure will not surface it.

The Signals That Indicate a Loop Problem

Three measurements indicate loop risk: session token count significantly above the baseline for that workflow type, elapsed session time exceeding the expected task completion window, and model call count per session above historical norms. Any one of these in isolation may be a false positive. All three together is a reliable loop signature.

How to Set Loop Depth Thresholds

Every workflow has a natural loop depth range. A document summarization workflow that normally completes in 1 to 3 model calls and occasionally hits 5 looks very different from one suddenly averaging 30. Establishing per-workflow baselines and alerting on deviations gives you the signal before it becomes a cost event. A threshold of 3x the historical average is a reasonable starting point for most teams.

What to Do When You Detect a Loop

Detection without remediation is incomplete. When a loop alert fires, the immediate questions are: what success condition is the agent failing to satisfy, is the input data within the expected range the agent was designed to handle, and has the prompt design changed recently. Most runaway loops trace to one of these three causes.