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For a practical implementation of the `CreateAction` model, refer to the [ticket-classification sample](https://github.com/UiPath/uipath-langchain-python/tree/main/samples/ticket-classification). This sample demonstrates how to create an action with dynamic input.
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#### Attributes:
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-**action** (Action): The instance of the action to wait for.
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-**app_folder_path** (Optional[str]): The folder path of the app.
An agent can invoke itself if needed, but this must be done with caution. Be mindful that using the same name for invocation may lead to unintentional loops. To prevent recursion issues, implement safeguards like exit conditions.
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For a practical implementation of the `InvokeProcess` model, refer to the [multi-agent-planner-researcher-coder-distributed sample](https://github.com/UiPath/uipath-langchain-python/tree/main/samples/multi-agent-planner-researcher-coder-distributed). This sample demonstrates how to invoke a process with dynamic input arguments, showcasing the integration of the interrupt functionality within a multi-agent system or a system where an agent integrates with RPA processes and API workflows.
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#### Attributes:
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-**job** (Job): The instance of the job that the agent will wait for. This should be a valid job object that has been previously created.
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-**process_folder_path** (Optional[str]): The folder path of the process.
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>**Note:** Ensure that the display names for the coder and researcher agent processes are *coder-agent* and *researcher-agent*,
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and that all 3 agents are placed in the same folder.
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>**Warning:** An agent can invoke itself if needed, but this must be done with caution. Be mindful that using the same name for invocation may lead to unintentional loops. To prevent recursion issues, implement safeguards like exit conditions.
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7. **Run the Planner Agent with Any Input Question**
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>**Tip:** For a five-step action plan, consider using the following input:
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