Jan 16, 2026·9 min read

Category:Automation›Agents

Why agents loop forever (and how to stop it)

A production playbook for preventing infinite loops: bounded retries, stop conditions, error classification, and escalation that actually helps humans.

Download available. Jump to the shipped asset.

An agent loop is not a cute demo problem.

In production it looks like repeat execution: the same tool call, the same error class, and a cost line that keeps climbing until someone notices. This post is not a tutorial. It is an operational playbook for stopping loops with runtime guardrails: stop rules, bounded retries, loop detection, escalation payloads, and a logging schema you can query at 2 AM.

If you run agents as automation, treat looping as an incident class. Your job is to prevent recurrence.

The incident pattern (what actually happens)

Loops usually start small, which is why teams miss them.

Timeline you have probably seen:

• 00:00: an agent hits a flaky dependency and retries
• 00:03: retries become a pattern (same request shape, same error)
• 00:10: the agent is no longer making progress, but it is still producing load
• 00:20: tokens and tool calls pile up, and on-call gets paged for the downstream system

The most damaging part is not the failure. It is the repeat behavior. A looping agent turns one bad response into ongoing backpressure.

This is the lane we care about: stopping repeat and silent failures, not teaching someone how to build an agent.

Why loops happen (mechanisms, not prompts)

Most loops are created by missing constraints in the runtime. Prompts can nudge behavior, but prompts do not enforce budgets. When the system has no hard limits, the model will keep searching for progress because that is the only available move.

The second cause is hidden retry amplification. Teams add retries in multiple places, then wonder why the agent "never stops".

Common mechanisms behind loops:

1) "Done" is not a state transition

If your system cannot detect completion as a state transition (record written, ticket closed, message sent with confirmation), the agent has no termination condition. It will keep exploring or re-trying until it hits a timeout you did not plan.

Vague goals create the worst loops. "Fix the issue" and "make it better" are not machine-detectable completion states.

2) Retry amplification is built into the stack

If you have retries in the HTTP client, retries in the tool wrapper, and retries in the agent policy, you have created a retry amplifier. Under backpressure this turns into a loop that looks like persistence.

This is why loops show up in production first: real 429s, real timeouts, real partial failures, and real concurrency.

3) Failure classes are not mapped to actions

If your agent treats every error as transient, it will keep trying on failures that cannot improve with retries.

The fastest way to create a loop is to retry on auth, validation, or policy blocks. Those are stop rules, not retry events.

4) Side effects are not idempotent

When tools have side effects (email, tickets, payments) and do not support idempotency keys, repeat execution becomes dangerous. The safe default is to stop early, escalate, and require human confirmation.

This is the part teams avoid because it feels like "slowing down" the agent. In production it is the opposite. Idempotency is what lets you retry safely.

Diagnosis ladder (fast checks first)

Diagnosis is not about understanding the model. It is about proving repetition and identifying which guardrail should have stopped it.

1) Confirm a loop fingerprint

Start with what is repeating:

• same tool name
• same error class
• same output shape
• same plan text

In practice you can detect this with a simple fingerprint of the last tool call and the last tool result. If the fingerprint repeats N times, you are not progressing.

This is operator-friendly. You do not need taste or intuition. You need a repeat counter.

2) Identify the trigger

Most loops start after one event:

• a tool starts returning 429
• a dependency starts timing out
• a permission changes
• an input becomes invalid

If you cannot name the trigger, your logs are too thin. Log the error class, not just the raw message.

3) Check for side effects (blast radius)

If the agent can perform irreversible actions, treat the loop as a safety incident.

Stop the blast radius first. Debug second.

Stop, retry, escalate defaults (the decision framework)

This is the core reliability posture: the agent must have explicit stop rules and a bounded retry budget. Anything else is wishful thinking.

The mapping below is deliberately boring. It is also what prevents recurring incidents.

• validation errors -> STOP (fix inputs)
• auth/permission -> ESCALATE (humans must fix scope)
• 429 -> RETRY with backoff + jitter (bounded)
• 5xx/timeouts -> RETRY limited, then ESCALATE
• safety/policy blocks -> STOP or ESCALATE (never retry)

If you want this to hold under pressure, encode it as a policy table in the runtime.

Two notes that save teams:

First, retries are not a feature. Retries are a controlled debt you pay down with budgets.

Second, escalation is not failure. Escalation is a successful stop rule that preserves safety and gives humans enough context to finish.

Prevention playbook (guardrails in code)

Guardrails do not need to be complex. They need to be enforceable.

1) Bound the run

Hard caps make behavior predictable:

• max steps per run
• max tool calls per run
• max retries per tool
• max wall clock time
• max token or cost budget

When a cap is hit, the agent must STOP or ESCALATE. Never keep trying.

This is what prevents the worst failure mode: a silent, expensive loop that runs until someone notices a cost spike.

2) Add a loop detector

Loop detection is just repeat detection:

• track loop_iteration
• track a fingerprint of the last action + last result
• stop when the fingerprint repeats beyond a small threshold

Minimal example:

type LoopState = {
  iteration: number;
  lastFingerprint?: string;
  repeatCount: number;
};
 
export function updateLoopState(state: LoopState, fingerprint: string, maxRepeats: number) {
  const nextIteration = state.iteration + 1;
  const nextRepeatCount = fingerprint === state.lastFingerprint ? state.repeatCount + 1 : 0;
 
  if (nextRepeatCount >= maxRepeats) {
    return {
      state: { iteration: nextIteration, lastFingerprint: fingerprint, repeatCount: nextRepeatCount },
      decision: "stop" as const,
      reason: `loop detected: fingerprint repeated ${nextRepeatCount + 1} times`,
    };
  }
 
  return {
    state: { iteration: nextIteration, lastFingerprint: fingerprint, repeatCount: nextRepeatCount },
    decision: "continue" as const,
  };
}

This is not advanced, but it changes the failure mode. Instead of "spin until the budget runs out", you get deterministic termination.

3) Make retries observable and jittered

Retries must be:

• bounded (a small integer, not "until success")
• jittered (avoid synchronized hammering)
• observable (you can answer "how many retries happened" quickly)

If you cannot query retry counts per tool call, you will debug under stress.

4) Escalate with a useful payload

Escalation should look like an operator handoff, not an apology.

Include:

• last actions (tool + parameters summary)
• last errors (status + message)
• retry counts and loop counters
• what would have made this succeed (permission, input, dependency health, human approval)

This is how you keep loops from becoming silent failures. The agent either completes, or it escalates with enough context that a human can finish quickly.

What to log (fields you can query at 2 AM)

If your plan is "add observability" without a schema, you are not shipping a fix. You are shipping hope.

At minimum, log:

• run_id
• goal
• decision (stop | retry | escalate)
• tool_calls_count
• tokens_used
• duration_ms
• error_class
• loop_iteration
• last_tool
• last_result_hash

These fields let you answer:

• is it looping
• why did it stop
• what guardrail would have prevented this

Example event shape:

{
  "ts": "2026-01-15T10:02:03.456Z",
  "run_id": "run_8f2c",
  "goal": "Draft a response to ticket #1842",
  "decision": "retry",
  "tool_calls_count": 7,
  "tokens_used": 3810,
  "duration_ms": 91234,
  "error_class": "rate_limit",
  "loop_iteration": 5,
  "last_tool": "kb.search",
  "last_result_hash": "b2f9...",
  "retry_after_ms": 1200
}

Shipped asset

This post ships a printable checklist and an operator decision tree.

If same tool + error repeats 3+ times -> STOP
If 429/timeout (bounded) -> RETRY with backoff + jitter
If auth/permissions/validation -> STOP (will not improve)
If unknown/unclear -> ESCALATE to human review
If loop depth > max_iterations -> STOP (kill switch)

Resources

This section is intentionally short. The detailed package breakdown is on the resource page.

• Loop guardrails checklist + decision framework
• AI Agents hub
• Axiom (Coming Soon)
• Backoff + jitter: the simplest reliability win

External references:

• OpenAI guide: building agents
• Idempotency keys (Stripe docs)
• Retry-After header (MDN)

FAQ

Coming soon

If you want more production-grade agent assets (guardrails, runbooks, templates), the Axiom waitlist is where they ship first.

This is not for prompt tweaks. It is for operational defaults you can enforce and hand to on-call.

Key takeaways

Loops are repeat failures, not model failures.

Make the behavior deterministic:

• define "done" as a state transition
• bound retries and total work
• classify failures before acting
• detect repetition and stop
• escalate with enough context for humans to finish

For more production agent reliability work, see the AI Agents category.

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