AutoGen Studio - Build Self-Improving AI Agents With No-Code
AI Summary
Summary: Multi-Agent Systems and Autogen
Multi-Agent Systems
- Autonomous vehicles need to coordinate as a multi-agent system.
- Experts discuss the importance of agents communicating with each other.
- Future AI will involve collective intelligence with agents aware of and communicating with each other.
Autogen for Non-Programmers
- Latest version of Autogen is designed for non-programmers.
- Installation and running API models like Mistol and Gemini Pro are demonstrated.
- Three key factors to maximize Autogen use are highlighted.
Factor 1: Flipping the Script
- Shift from single intelligent AI agents to a future of collective intelligence.
- Questions will evolve from prompting techniques to communication flows among agents.
- Platforms like Autogen and Crei are paving the way for this future.
Factor 2: Architecting Autogen
- Step-by-step tutorial on installing Autogen and setting up a virtual environment.
- Autogen Studio installation and environment variable setup are explained.
- Four building blocks of Autogen: Agent, Model, Skills, and Workflow.
- Workflows can be hierarchical or non-hierarchical.
Factor 3: Sledgehammer to Crack a Nut
- Autogen’s full potential lies in building systems, not just smart agents.
- Agents can autonomously build new skills for themselves.
- Self-improving agents are a key feature of Autogen.
Building Systems with Autogen
- Workflow consists of Architect, Reviewer, and Optimizer agents.
- Agents can be designed to iteratively improve outputs.
- Autogen allows for the creation of self-improving agents.
Accessing API Models and Open Source Models
- Light LLMP project can wrap APIs to look like OpenAI API.
- Running open-source models locally for free is possible with LM Studio.
- Small open-source models struggle with function calling due to limited cognition.
Function Calling and Open Source Models
- Function calling allows models to interact with external functionalities.
- Some small open-source models are fine-tuned for function calling.
- Trisis Llama 2 is recommended for multi-agent systems due to its ability to use skills effectively.
Final Thoughts
- AGI might emerge from a society of intelligent, self-improving agents rather than from training larger models.
- The future of AI involves systems of agents automating tasks, leading to scientific innovation, and potentially achieving general intelligence.