Summary of A Multi-ai Agent System For Autonomous Optimization Of Agentic Ai Solutions Via Iterative Refinement and Llm-driven Feedback Loops, by Kamer Ali Yuksel and Hassan Sawaf
A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops
by Kamer Ali Yuksel, Hassan Sawaf
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Multiagent Systems (cs.MA); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a framework for autonomously optimizing Agentic AI systems across industries, leveraging an LLM (Llama 3.2-3B) to refine roles, tasks, and interactions without human input. The framework employs agents for Refinement, Execution, Evaluation, Modification, and Documentation, achieving optimal performance through iterative feedback loops. This approach enhances scalability and adaptability, offering a robust solution for real-world applications in dynamic environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This framework can optimize Agentic AI systems across industries like NLP-driven enterprise applications. It uses agents to refine roles, tasks, and interactions without human input, making it efficient and scalable. The system achieves this by generating and testing hypotheses to improve system configurations. |
Keywords
» Artificial intelligence » Llama » Nlp
