"Research agent 3.0 - Build a group of AI researchers" - Here is how

Summary

The video script details the development of an advanced AI research system, AI Researcher 3.0, which utilizes multiple GPT assistants with distinct roles to perform comprehensive research tasks. The system is designed to extract data from an Airtable, conduct internet research, and compile findings back into the Airtable. It features a director, research manager, and research agent, each responsible for delegating tasks, quality control, and data gathering. The system's flexibility allows for the addition of more agents to improve functionality. The script also discusses the evolution of AI research tools, the challenges of quality consistency and memory management, and the use of platforms like Gradient AI for fine-tuning models. The presenter is enthusiastic about the potential applications of such a system, particularly for sales and venture capital lead qualification.

Takeaways

• 🚀 **Building AI Research Teams**: The script describes the creation of a multi-agent system for conducting research, which includes different specialized roles like a director, research manager, and research agent.
• 🔍 **Data Extraction and Research**: The system is designed to extract data from an Airtable, conduct research on various topics, and input findings back into the Airtable.
• 🤖 **AI Agent Capabilities**: The AI agents are capable of breaking down large research goals into subtasks, browsing the internet for information, and generating high-quality research.
• 📈 **Evolution of AI Research Tools**: The speaker discusses the evolution of their AI research tools over the past six months, highlighting improvements in capability and output quality.
• 🔗 **Link Analysis and Scripting**: The research agent is programmed to script websites to extract detailed information and is cautious about the quality of sources, avoiding those that may be unreliable.
• 🔄 **Iterative Research Process**: The process involves iterative searching and scripting, with a limit of three iterations to ensure thoroughness without unnecessary repetition.
• 📚 **Quality Control and Feedback Loop**: The research manager plays a crucial role in quality control, reviewing research outputs and providing feedback to ensure comprehensive and accurate results.
• 📊 **Structured Data Management**: The director agent is responsible for managing structured data, breaking down tasks, and updating an Airtable with the research findings.
• 💡 **Autogen Framework**: Autogen is utilized to orchestrate the collaboration between different agents, simplifying the use of the Assistant API and allowing for a more flexible system design.
• 📈 **Scalability and Expansion**: The system is scalable and can be expanded with more agents to handle increasingly complex tasks and improve the consistency of research quality.
• 💻 **Technical Requirements**: The setup requires specific technical knowledge and tools, such as the use of APIs for Google search and web scripting, as well as the handling of Airtable records.

Q & A

What is the main purpose of the AI researcher system described in the transcript?

The main purpose of the AI researcher system is to automate the research process by extracting data from sources like Airtable, conducting internet searches, and compiling high-quality research results back into Airtable, all while being able to expand and improve its capabilities over time.

How does the AI researcher system handle complex research tasks?

The system handles complex research tasks by breaking down big research goals into subtasks, using multiple agents that work together to perform different specialized tasks such as browsing the internet, critiquing, quality control, and managing the research process.

What is the role of the 'director' in the multi-agent research system?

The 'director' in the multi-agent research system is responsible for extracting research topics from an Airtable database, breaking down research tasks, and delegating these tasks to the research manager and researchers. It also updates the Airtable records once the research is completed.

How does the 'research manager' contribute to the quality of the research?

The 'research manager' contributes to the quality of the research by generating a research plan for a given topic, reviewing the results delivered by the researcher, and pushing back if the information is insufficient or lacks depth, ensuring a higher standard of research output.

What is the function of the 'researcher' agent in the system?

The 'researcher' agent is tasked with browsing the internet, performing searches, and scripting websites to gather detailed information on a given topic. It follows a set of instructions to ensure thorough research and fact-based results.

How does the system ensure consistency in the quality of research results?

The system ensures consistency in the quality of research results through a multi-layered approach involving a research manager for quality control, a director for task delegation, and the use of multiple agents working in tandem to critique and refine the research process.

What are the challenges faced by the AI researcher system?

The challenges faced by the AI researcher system include memory limitations, where the researcher might forget previously found information, and the potential high cost of running multiple searches and operations, which requires careful monitoring of the OpenAI usage.

How does the system expand its capabilities for different working groups?

The system expands its capabilities by introducing more agents into the assistant, such as a research director who can break down large research goals into subtasks and delegate them to both the research manager and researchers, thus increasing the system's ability to handle more complex tasks.

What is the significance of using Autogen in the creation of the multi-agent research system?

Autogen simplifies the use of the Assistant API by providing a straightforward way to trigger messages and manage the progress of tasks without the need for continuous checking. It also allows for the creation of various hierarchies and structures to orchestrate collaboration between different agents.

How does the system handle the potential for inconsistent quality in research results?

The system addresses inconsistent quality by incorporating a research manager that critiques and reviews the research output, ensuring that the results are always aligned with the user's expectations and that the researcher explores all possible avenues to find the necessary information.

What are the two common ways to train highly specialized agents?

The two common ways to train highly specialized agents are fine-tuning, which improves the model's skills in performing specific tasks, and creating a knowledge base, also known as Retrieval-Augmented Generation (RAG), which provides the model with very accurate and recent data.

How does the use of Grading AI simplify the process of fine-tuning open-source models?

Grading AI simplifies the process of fine-tuning open-source models by making it extremely simple and accessible. It removes the need for dedicated infrastructure and computing units, allowing developers and enterprises to only pay for what they use by token.