In this example, we load training data from a Markdown file and create an agent instance with an empty list of policies and no interpreter. The `domain.yml` file contains domain-specific information such as intents, actions, and templates. 2. Training the agent with the loaded data:python
In this example, we call the `train` method on the agent object to train it with the loaded training data. 3. Handling user inputs and generating responses with the trained agent:python from rasa.core.agent import Agent agent = Agent.load("models/dialogue", interpreter=None) response = agent.handle_message("Hello") print(response) ``` In this example, we load a trained agent from a persisted model directory (`models/dialogue/`), and use the `handle_message` method to pass a user message ("Hello") to the agent and receive a response. The `rasa.core` package library contains the `Agent` class as well as other core components for building conversational AI models, such as `Tracker`, `Domain`, `RulePolicy`, `FallbackPolicy`, etc.