NEW: AI Agents That Design Themselves w/ ADAS

AI Agents that create and design themselves: Explore a novel framework for the automated design, evaluation, and refinement of AI agents through iterative collaboration and self-improvement processes.

The system leverages advanced language models to generate agent architectures that consist of multiple specialized modules, each embodying expertise in specific domains such as physics, chemistry, and biology.

The framework operates by initiating a task that each expert module approaches independently, generating initial solutions through step-by-step reasoning. These solutions are then subjected to a cross-examination phase where peer critic modules analyze and provide constructive feedback on each other's outputs, identifying potential errors and areas for improvement.

In response to the critiques, the expert modules refine their solutions, incorporating insights and corrections to enhance accuracy and reliability. A final decision module then aggregates the refined solutions, performs comprehensive reasoning, and produces a cohesive and optimized answer to the original task.

This iterative process fosters a collaborative environment where AI agents can self-improve by learning from peer feedback and refining their reasoning strategies. The automated nature of the design and evaluation pipeline accelerates the development of robust AI systems capable of complex problem-solving through collective intelligence and adaptive learning.

The results demonstrate the effectiveness of this approach in creating sophisticated AI agents that exhibit enhanced performance and adaptability across various domains, highlighting the potential for scalable and automated AI development methodologies in tackling increasingly complex challenges in all industrial and medical areas.

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All rights with authors:
Automated Design of Agentic Systems
https://arxiv.org/pdf/2408.08435

GitHub Code:
https://github.com/ShengranHu/ADAS

00:00 Automated Design of Agentic Systems
03:19 SOTA Hand-design Agents vs Meta Agent Search
05:33 GitHub Code repo ADAS
06:33 Real Code and Prompt for Meta Agent
09:35 Self-Reflection Prompt of Meta Agent
10:35 Framework Code
12:21 Python code for Review Agent (Minion)
14:45 GPT-4o explains Python Code of Agent
21:14 Template for an AI Agent: class LLMAgentBase
25:10 Search Function is central to refine Agents
26:23 Example of an Agent Design
28:12 Special Bonus: NEW Open Researcher for accelerating Research