NEW Multi-Agent CODE explained (by OpenAI)

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.

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Orchestrating Agents: Routines and Handoffs
CODE:

Nice idea by @OpenAI

#openai
#airesearch
#aiagents

[HD-hfgr]

Brother you are doing some good work. Discover AI is becoming one of my favorite AI channels.

[jmarkinman]

Fantastic overview! Thank you for your work.

[tvwithtiffani]

People who know how to code have already been doing this. My question for openai would be why are they offering the public crumbs? What is their motivation. They have chosen to keep the good stuff secret for as long as they can. Like seriously, OpenAI is the only AI company that hasn't open sourced a language model. Not even a small one since gpt-2

[MrAloha]

I love your videos, thank you! I've been doing what you describe in this video in my chatbot systems that are made up of multiple agents running on persona prompts. The tool that makes this possible is GPT-Trainer. They've had multi agent systems available for a year now, and the agent handoffs are managed by the AI Supervisor that is prompted, and the hand-off is based on user intent. The intent is determined by the words being used. Very similar to what you have described. Thanks for these videos, I'm learning a lot from you. Make it a great day! 💪😁🌴🤙

[YourBrandDead]

First off, love your channel. Im creating a multi agent.. multi-assistant app that replicates the different departments of a record label. Can I plug my assistants that I trained into the triage agent format by using the assistant id?
- I want to have a manager agent that reviews the users request, if it’s a simple question it’ll answer, if not it’ll pass it down to the proper agent/assistant(department). Am I thinking of this right?
(Side note: my cofounder is Malik Yusef, Ye’s main collaborator, Ima simple creator that’s always creating things outside my wheelhouse but you have made this learning process much easier, I’ve only created gmail account before this, so your level of teaching 🏆🥇I appreciate you more than you know🙏🏼)

[alexanderzikal]

Sending private data to a server is not always an option. Yesterday was Google in Tokio with a lot of demos. Small models running offline, but nearly always Python3 is used. We need better Interfaces/Libs for compiled languages. It is possible in C++ but not easy. GoLang, C/C++, Rust, Swift, Kotlin or JAVA. JAX for other languages…

[BioniChaos]

I know it's about LLMs so there is so much text in the video/slides but is there a deployed demo if this?

[solyarisoftware]

Thank you for your videos – they’re always insightful! I recently published an article titled "SWARMing Conversational AI: Integrating No-Code and Code in Agent-Based Workflows, " which you can find on LI. In it, I discuss some strengths as well as certain potential weaknesses and antipatterns that arise. It might be interesting to you and could even inspire ideas for future videos. 😊

[TheQuantumOxymoronIAMAI]

... or you just instruc the LLM to create the flow of Agents needed to complete the assigned task ...