An agent stuck in a retry loop doesn't look broken — it looks busy. Requests go out, responses come back, counters increment. The failure is that none of it is making progress. By the time a human notices, the cost or rate-limit damage may already be done.
Agents fail in loops for several distinct reasons, and they tend to compound each other.
An external API is rate-limited, returning a malformed response, or temporarily unavailable. The agent's retry logic fires, as designed. Without a ceiling on how many genuine retries are allowed, the agent continues retrying — each attempt identical to the last, none of them likely to succeed until the underlying condition changes.
In autonomous agent configurations, the model may not have a reliable mechanism to recognize that it is repeating itself. Each call feels, from inside the inference loop, like a reasonable next step. The agent is not broken — it simply lacks the external vantage point to observe that the same action has already been attempted without result.
When an agent delegates to a sub-agent, which delegates further, the resulting call stack can grow in ways that are invisible to any individual participant. Each layer sees only its own level. Without enforcement at each layer against a shared depth counter, there is nothing in the structure itself to cap how far the nesting goes.
Each failed retry typically still costs something — an API call, a model inference, a tool invocation. An agent retrying many times against a failing endpoint may spend more on the loop than on the successful work it was originally asked to do.
Adding retry guidance to a system prompt is a reasonable starting point. The problem is that the model processes this instruction in context, alongside everything else in the prompt and the conversation. Under task pressure, with a long context window, or after many turns, prompt instructions are weighted — they are not enforced.
Runtime enforcement operates independently of the model. It does not ask the model whether it has retried too many times. It maintains counters in external state, enforces configured limits against them, and returns a verdict before the action runs — independently of what the model believes about its own progress.
The general approach is to insert a check into the execution path, between the orchestration layer and the tool call, that enforces limits against runtime state the model cannot modify. That check tracks counters across calls and blocks actions when configured limits are reached.
Building this for production involves real engineering: state that survives restarts, configurable policies, correct handling of concurrent agent activity, enforcement visibility, and a mechanism to clear a paused agent after investigation. Each of those is a distinct problem.
Retry limits cap how many times an agent can genuinely retry a failed action. When the limit is reached, the agent stops and requires a manual clear — preventing further calls until the underlying issue is investigated.
Step caps per chain bound the total number of actions a chain can take, regardless of whether individual steps are retries. An agent that successfully completes each step but keeps going without converging hits a step cap even if it never retries anything.
Chain depth limits address recursive delegation. Each layer of a delegated agent stack reports its nesting depth. A runtime check at each level against a configured ceiling stops the stack from growing beyond a set maximum, regardless of which layer initiated the delegation.
Cost and volume pressure provide early signals before hard limits are reached. A configurable warning threshold gives the orchestration layer a chance to slow down before a hard stop fires.
Redlynr implements these limits as a dedicated API. Your orchestration layer calls POST /run before each agent action. Redlynr evaluates retry count, step count, chain depth, cost accumulation, and request volume, and returns proceed, slow_down, or stop. The check enforces configured limits independently of the model. If a retry limit is reached, the agent is paused and requires a manual clear via POST /pause before it can proceed.
Redlynr also supports optional behavioral controls for repeated or fixated tool-use patterns. These complement the hard limits on retries, steps, depth, cost, and volume, and are disabled by default.
The default limits — 2 retries per agent, 20 steps per chain, depth 5, $50/24h tenant ceiling — apply immediately after registration with no policy configuration required.
Runtime loop prevention is most valuable when agents run unattended, when tool calls have real cost, and when a stuck agent cannot be caught quickly by a human in the loop. If you are running short, human-supervised sessions, prompt-level guidance may be sufficient. If agents run at scale or with meaningful spend per action, a runtime enforcement layer is worth the integration cost.
Redlynr costs $0.001 per /run call, with no subscription. Your first 30 calls are free — no account required. A Python SDK (pip install redlynr) puts enforcement directly in the execution path. The REST API works with any language.