Vulnerability CVE-2025-55182 and protection measures

Title: "Agent stopped due to max iterations" — What It Means and How to Handle It

Introduction

The message "Agent stopped due to max iterations" indicates that an agent (a machine learning, optimization, or search algorithm) has terminated because it reached the iteration limit predefined in its settings. On its own, this message is not very informative: it does not explain why no other stopping condition (such as an error or convergence) was met, does not show the current state of metrics, and does not suggest next steps. In this article, I will explain what this message means, the potential causes behind it, and what actions to take to diagnose and resolve the situation.

Main Part

1. What exactly the message means

   The agent was performing an iterative process (training, optimization, simulation) and was forcibly stopped because the number of completed iterations reached a predefined limit (the max_iterations parameter or equivalent).

   This is not necessarily an error: the limit may be intentionally set to save time or resources. However, it can also indicate that the process did not converge or that the stopping conditions were defined incorrectly.

2. Potential causes

   • Incorrectly set parameters:
     The default iteration limit is either too low or too high for the specific task.
   • Missing or broken convergence logic:
     The logic for checking convergence criteria (e.g., based on loss, gradient, or improvement of a target metric) is either absent or not functioning.
   • Unsuitable architecture/hyperparameters:
     Too small a learning rate, poor initialization, or insufficient information in the data can all slow down convergence.
   • Code bugs:
     Infinite loops, errors in state updates, or incorrect calculation of stopping conditions.
   • Incorrect resource requirements:
     The agent lacks sufficient computing power or time, leading to a strict limit being imposed.

3. How to diagnose the problem

   • Review the logs:
     Check target function/error values per iteration, validation metrics, warnings, and errors.
   • Check for progress:
     Determine if metrics were improving in the final iterations or if the process was completely "stuck."
   • Compare with baseline runs:
     Check if convergence is usually achieved before reaching the specified limit in similar scenarios.
   • Check parameters:
     Review max_iterations, tolerance, patience, and other configuration parameters.
   • Write simple tests:
     Run the agent on a small dataset or model where fast behavior is expected to ensure the stopping logic works.

4. Possible solutions and recommendations

   • If the goal is to give the agent more time:
     Increase max_iterations, but do so mindfully while monitoring metrics.
   • Configure convergence stopping criteria:
     Add a threshold for target function changes (tolerance) or an early stopping mechanism (patience) so that termination is based on quality, not just the iteration count.
   • Improve hyperparameters:
     For example, adapt the learning rate, use adaptive step optimizers, apply regularization, normalize inputs, or use a more suitable model.
   • Optimize the code:
     Profile "hot" sections of the code, eliminate memory leaks, parallelize computations, and simplify calculations to speed up iterations.
   • Add detailed logs:
     Record key metric values every N iterations, time per iteration, and the specific reason for stopping (limit reached, convergence, error).
   • Implement version control for configurations and experiments:
     This ensures you know exactly which parameters were used for every run.

5. Action plan for when the message recurs

   1. Evaluate logs and metrics from the final iterations.
   2. If metrics are improving but slowly — increase max_iterations or improve convergence speed.
   3. If metrics are not improving — investigate hyperparameters, architecture, and data.
   4. If a bug is suspected — reproduce the issue with a simplified example and cover the problematic area with tests.
   5. Add or improve notifications/logs so that the next run provides more context.

Conclusion

The message "Agent stopped due to max iterations" does not provide the full picture on its own. It simply means the process ended due to the iteration count; however, the true cause (intentional limitation, slow convergence, or a bug) requires further diagnosis. It is recommended to check logs, configure convergence criteria, adjust hyperparameters, and improve system observability (monitoring) to receive informative messages about stopping reasons and make informed decisions in the future.

If needed, I can rewrite a specific log message so that it immediately includes the current metric value, iteration number, and an action recommendation — please send a log sample.