---
title:  Orchestration
---
## Error handling
ControlFlow provides mechanisms to handle errors and exceptions that may occur during task execution.

In eager execution mode, if a task fails and raises an exception, you can catch and handle the exception using Python's standard exception handling techniques, such as `try`-`except` blocks.

Here's an example of error handling in eager mode:

```python
from controlflow import flow, task

@task
def divide_numbers(a: int, b: int) -> float:
    """Divide two numbers."""
    pass

@flow
def my_flow():
    try:
        result = divide_numbers(10, 0)
        print(result)
    except ValueError as e:
        print(f"Error: {str(e)}")
```

In this example, if the `divide_numbers` task fails because the agent recognizes that it can't divide by zero, it will raise a `ValueError`. The exception is caught in the `except` block, and an error message is printed.

In lazy execution mode, exceptions are not raised immediately but are propagated through the task DAG. When the flow is executed using the `run()` method, any exceptions that occurred during task execution will be raised at that point.

## Observability and orchestration
ControlFlow integrates with Prefect, a popular workflow orchestration tool, to provide observability and orchestration capabilities for your workflows.

Under the hood, ControlFlow tasks and flows are modeled as Prefect tasks and flows, allowing you to leverage Prefect's features for monitoring, logging, and orchestration. This integration enables you to track the execution status of tasks, monitor their progress, and access detailed logs and reports.

By default, ControlFlow configures Prefect to use a local SQLite database for storage and a ephemeral Prefect server for orchestration. You can customize the Prefect configuration to use different storage backends and orchestration setups based on your requirements.

To access the Prefect UI and view the status and logs of your workflows, you can start the Prefect server and open the UI in your web browser:

```bash
prefect server start
```

Once the server is running, you can open the Prefect UI by navigating to `http://localhost:4200` in your web browser. The UI provides a visual interface to monitor the execution of your workflows, view task statuses, and access logs and reports.

## Best practices
When designing workflows in ControlFlow, consider the following best practices:

1. **Define clear task objectives**: Ensure that each task has a clear and specific objective. Provide detailed instructions and context to guide the AI agent in completing the task effectively.

2. **Choose the appropriate API**: Decide whether to use the functional or imperative API based on your workflow requirements. The functional API is suitable for simple workflows and quick prototyping, while the imperative API is beneficial for more complex workflows and advanced customization.

3. **Modularize tasks**: Break down complex tasks into smaller, modular subtasks. This promotes reusability, maintainability, and easier debugging.

4. **Handle errors gracefully**: Implement proper error handling mechanisms to deal with exceptions and task failures. Use `try`-`except` blocks in eager mode and handle exceptions appropriately in lazy mode.

5. **Leverage observability features**: Utilize the observability and monitoring capabilities provided by Prefect. Access the Prefect UI to track task execution, view logs, and gain insights into workflow performance.

6. **Test and debug workflows**: Thoroughly test your workflows to ensure they behave as expected. Use debugging techniques, such as logging statements and breakpoints, to identify and fix issues.

7. **Optimize performance**: Analyze and optimize the performance of your workflows. Identify bottlenecks, minimize unnecessary computations, and leverage parallelism and caching when applicable.

8. **Document and version control**: Maintain clear documentation for your workflows, including task descriptions, dependencies, and usage instructions. Use version control systems to track changes and collaborate effectively.

By following these best practices, you can design robust, efficient, and maintainable workflows in ControlFlow.
