AI Agent Stuck in a Loop: How to Stop and Prevent

An AI agent stuck in a loop repeatedly executes the same action — re-reading the same file, retrying a failed API call, or rewriting the same code block — burning tokens, time, and money without making progress. SafeClaw by Authensor detects repeated action patterns and enforces iteration limits at the policy level, automatically breaking loops before they spiral out of control. This works across both Claude and OpenAI-powered agents with zero code changes.

Why AI Agents Get Stuck in Loops

AI agents decide their next action based on the output of their previous action. When the output is ambiguous, unchanged, or contradictory, the agent can enter a cycle:

• Error-retry loops: The agent encounters an error, retries the exact same action, gets the same error, and retries again.
• Self-correction loops: The agent writes code, tests it, finds a bug, "fixes" it by reverting, tests again, finds the same bug.
• Tool-call loops: The agent calls a tool, misinterprets the result, and calls the same tool with identical parameters.
• Goal-conflict loops: Two competing objectives cause the agent to alternate between contradictory actions.

Immediate Fix: Stop the Loop

1. Kill the Agent Process

If the agent is currently looping, terminate it. SafeClaw's audit trail will preserve the full action history even after termination.

2. Review What Happened

npx @authensor/safeclaw audit --last 50

Look for repeated entries with the same action and resource fields. This confirms a loop.

3. Install SafeClaw (If Not Already)

npx @authensor/safeclaw

Prevent Future Loops with Policy Rules

Set Iteration Limits

Add loop detection to your safeclaw.policy.yaml:

limits:
  max_repeated_actions: 3     # same action+resource combo
  max_total_actions: 100      # total actions per session
  max_execution_time: 300     # hard time cap in seconds

rules:
  - action: "*"
    resource: "*"
    effect: allow
    max_repeats: 3
    reason: "Global repeat limit to prevent loops"

When an agent tries to perform the same action on the same resource more than 3 times, SafeClaw denies it and logs the event.

Use Action Cooldowns

For actions that should not repeat rapidly:

rules:
  - action: file.write
    resource: "/src/**"
    effect: allow
    cooldown: 10  # seconds between repeated writes to same file
    reason: "Prevent rapid rewrite cycles"

Enable Simulation Mode for New Tasks

Before letting an agent tackle an unfamiliar task, run in simulation mode:

npx @authensor/safeclaw --simulate

If the simulation shows the same action repeating, you know the task definition needs refinement before real execution.

Troubleshooting Specific Loop Patterns

Agent keeps re-reading the same file:

The agent likely cannot parse the file contents correctly. Check if the file format matches what the agent expects. Add a read limit:

rules:
  - action: file.read
    resource: "/config/**"
    effect: allow
    max_repeats: 2
    reason: "Config reads should not repeat"

Agent retrying a failing API call endlessly:

Set both retry limits and timeouts:

rules:
  - action: network.request
    resource: "https://api.service.com/**"
    effect: allow
    max_repeats: 3
    timeout: 15
    reason: "API call with retry cap"

Agent alternating between two files (ping-pong pattern):

This is a multi-resource loop. Use the total actions limit to cap it:

limits:
  max_total_actions: 50

Agent rewriting the same code block over and over:

This often means the test the agent is trying to pass is flawed or the agent's approach is fundamentally wrong. The loop limit will stop the agent, and you can review the audit trail to see what it was attempting.

Why Deny-by-Default Prevents Loops

In an allow-by-default system, a looping agent runs until it exhausts resources. SafeClaw's deny-by-default model means every repeated action is evaluated. The 446-test suite validates that loop detection works correctly, and the hash-chained audit trail gives you tamper-proof evidence of what happened.

Loops are one of the most common and most expensive AI agent failures. Policy-level loop prevention is not optional — it is essential.

Related Resources

• AI Agent Timeout: Causes and Solutions
• AI Agent Cost Overrun: How to Set Budget Limits
• How to Monitor AI Agents
• Define: Deny-by-Default