UserAgent: Enhancing User Interactions in Conversational AI#

Overview#

UserAgent 🤖, a specialized variant of the BaseAgent, is crafted to simulate and elevate user interactions within conversational AI applications. It’s designed for flexibility, capable of either using preset user inputs or dynamically prompting for user responses.

Key Features#

  • Simulating User Input 💬: UserAgent can operate with predefined messages, mimicking user interactions. This feature is excellent for demonstrations or testing how an AI agent would engage with specific user inquiries.

  • Prompting for Real-Time Input 🎤: Alternatively, UserAgent can actively seek user input during its operation, adding a layer of interactivity and enabling genuine conversation flows between the user and the AI agent.

Basic Use Case: Interactive Customer Service Bot 🛒#

Let’s envision a customer service bot scenario:

  1. Predefined Queries for Demos 🎭: For showcasing the bot’s capabilities, you can configure UserAgent with pre-set customer questions. This allows the bot to demonstrate its ability to handle common customer interactions smoothly.

  2. Live Interaction with User Queries 🌐: In a live setting, set UserAgent to prompt users for their questions. This feature enables real-time interactions, providing an interactive and efficient customer service experience.

How It Works#

  • Start with User Input 💬: UserAgent initiates the flow by prompting for user input, setting the stage for a personalized conversation.

  • Flow of Agents 🔄: Following UserAgent, each agent in the sequence processes the input further, adding layers to the dialogue.

  • End with Comprehensive Response 🎉: The final agent in the sequence delivers a response that reflects the user’s initial input, culminating in a well-rounded interaction.

Example: Customer Query Handling Bot#

from comma_agents.agents import UserAgent, BaseAgent
from comma_agents.flows import SequentialFlow

# Subsequent agent for query processing
response_agent = BaseAgent(name="ResponseAgent")

# Create SequentialFlow with UserAgent
query_flow = SequentialFlow(
    flow_name="User Query",
    flows=[
        UserAgent(name="User Input", require_input=True),
        response_agent
    ]
)

# Execute the flow
response = query_flow.run_flow()
print(response)

Use Case#

This setup is ideal for a customer service bot that starts by asking the customer for their query and then processes it through different agents to provide a tailored response, enhancing customer engagement and satisfaction.

Summing Up#

UserAgent is a versatile and user-agent tool in the conversational AI. Whether used for scripted demonstrations or live, interactive experiences, it offers a solid foundation for building various user-engaged LLMs. 🌈👩‍💼🤖