New Multi-Agent orchestration by OpenAI. Code based video with detailed explanations.
This video revolves around the design and implementation of a multi-agent system for managing user interactions in tasks such as customer service, sales, and support.
The core concept involves defining routines, which are structured sequences of instructions for handling specific workflows. Each routine consists of a system message that outlines the steps the agent must follow, such as asking probing questions, proposing solutions, or offering refunds. To enable dynamic actions within the routine, the system integrates tools, which are Python functions used by agents to perform tasks like looking up an item or processing a refund. These tools are translated into JSON schemas so that the language model (e.g., OpenAI GPT-4) can invoke them as part of the conversation.
The conversation management aspect of the system is built around the idea of tool calls, where the model can determine when it needs to execute a tool based on the user’s input and the agent’s instructions. A function (execute_tool_call) is used to map the model’s tool requests to the appropriate Python function, allowing agents to interact with external systems or databases, simulate refunds, or search for items. Additionally, the system allows for handoffs between agents, where one agent can seamlessly transfer the conversation to another agent more suited to handle a specific request (e.g., from sales to support). This handoff mechanism is implemented using agent classes that can switch context based on the conversation’s flow, enabling a flexible and dynamic interaction process.
The discussion focuses on simplifying the intricate logic behind the multi-agent framework, making it more accessible for understanding. The role of agents and routines in managing distinct workflows was explained, along with the introduction of tools to bridge the gap between conversational AI and real-world actions. Additionally, the concept of handoffs was explored, demonstrating how they enable scalable and modular interaction flows, allowing different agents to take control based on the conversation’s context. This system architecture effectively combines the reasoning capabilities of language models with actionable Python functions, resulting in a robust and extensible solution for orchestrating complex interactions across domains such as customer service and sales.
All rights with authors:
Orchestrating Agents: Routines and Handoffs
https://cookbook.openai.com/examples/orchestrating_agents
CODE:
https://github.com/openai/openai-cookbook/blob/main/examples/Orchestrating_agents.ipynb
Nice idea by @OpenAI
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