NEW Multi-Agent CODE explained (by OpenAI)

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AI Summary

Video Summary

Latest State of Code Agents

• OpenAI recently updated their example of coding agents.
• Vision language models can constitute agents with the right tools (e.g., cameras, sensors).
• Agents can be enhanced with additional sensors to become superhuman (e.g., infrared, ultraviolet).

Key Characteristics of an Agent

• Model: A language model like GPT-4 or GPT-3.5-mini.
• Instruction: A system prompt guiding the agent’s behavior.
• Tools: Python functions the agent can call for actions or accessing external functionalities.

Multi-Agent Systems

• Agents have specific roles and access to specific tools.
• Conversational systems can transition seamlessly between agents.
• Agents can interact with a knowledge graph, which may be more efficient than a chain typology.

Elements of Agents

• Routines: Sequences of steps as instructions for tasks.
• Tools: Python functions for specific actions.
• Function to Schema Conversion: Utility to convert Python functions into a schema for the language model.
• Tool Calls: The model can call functions during a conversation.
• Handoffs: Agents can transfer conversations to other agents.
• Agent Class: Structure to define agents, instructions, tools, and models.
• Multi-Agent Orchestration: Code allowing multiple agents to interact within a single conversation.

Example of Multi-Agent Interaction

• Triage Agent: Handles initial customer interaction.
• Sales Agent: Assists with sales-related inquiries.
• Issues and Repairs Agent: Deals with product issues and repairs.
• Agents use defined functions to perform tasks and can hand off conversations to other agents as needed.

Main Loop of Agent Interaction

• User input is processed by the current agent.
• The agent responds and updates based on the conversation flow.
• Function calls and handoffs are managed within the loop.

Benefits of the System

• Modularity
• Dynamic handoffs
• Tool integration
• Context preservation
• Scalability

Summary by “Strawberry”

• The run_full_turn function is central to orchestrating interactions.
• It manages message passing, model interaction, function calls, and agent handoffs.
• The system is flexible, scalable, and can handle sophisticated conversational systems.

Code and Resources

• Complete Python notebook available at OpenAI’s GitHub repository.
• The notebook contains detailed examples and explanations of routines and handoffs.

Upcoming Content

• Future videos will explore professional communication protocols and configurations for multi-agent systems.

(Note: No specific CLI commands, website URLs, or detailed instructions were provided in the transcript.)