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A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops

Kamer Ali Yuksel, Hassan Sawaf

TL;DR

The study tackles the challenge of optimizing complex Agentic AI systems whose inter-agent dependencies and evolving objectives complicate manual tuning. It introduces an autonomous refinement framework powered by LLM-driven feedback loops, with dedicated components for synthesis, evaluation, and refinement that iteratively test and adopt better agent configurations. Across diverse case studies, the approach yields consistent gains in alignment, relevance, clarity, actionability, and efficiency, culminating in a best-known variant $C_{ ext{best}}$ chosen when the improvement exceeds a threshold $\epsilon$ or after a fixed number of iterations. The framework demonstrates scalable, domain-independent optimization for real-world, dynamic environments, though it acknowledges limitations related to model biases, criteria design, and computational demands. Supplementary data and evolved agent configurations are openly available for reproducibility and further exploration.

Abstract

Agentic AI systems use specialized agents to handle tasks within complex workflows, enabling automation and efficiency. However, optimizing these systems often requires labor-intensive, manual adjustments to refine roles, tasks, and interactions. This paper introduces a framework for autonomously optimizing Agentic AI solutions across industries, such as NLP-driven enterprise applications. The system employs agents for Refinement, Execution, Evaluation, Modification, and Documentation, leveraging iterative feedback loops powered by an LLM (Llama 3.2-3B). The framework achieves optimal performance without human input by autonomously generating and testing hypotheses to improve system configurations. This approach enhances scalability and adaptability, offering a robust solution for real-world applications in dynamic environments. Case studies across diverse domains illustrate the transformative impact of this framework, showcasing significant improvements in output quality, relevance, and actionability. All data for these case studies, including original and evolved agent codes, along with their outputs, are here: https://anonymous.4open.science/r/evolver-1D11/

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops

TL;DR

The study tackles the challenge of optimizing complex Agentic AI systems whose inter-agent dependencies and evolving objectives complicate manual tuning. It introduces an autonomous refinement framework powered by LLM-driven feedback loops, with dedicated components for synthesis, evaluation, and refinement that iteratively test and adopt better agent configurations. Across diverse case studies, the approach yields consistent gains in alignment, relevance, clarity, actionability, and efficiency, culminating in a best-known variant chosen when the improvement exceeds a threshold or after a fixed number of iterations. The framework demonstrates scalable, domain-independent optimization for real-world, dynamic environments, though it acknowledges limitations related to model biases, criteria design, and computational demands. Supplementary data and evolved agent configurations are openly available for reproducibility and further exploration.

Abstract

Agentic AI systems use specialized agents to handle tasks within complex workflows, enabling automation and efficiency. However, optimizing these systems often requires labor-intensive, manual adjustments to refine roles, tasks, and interactions. This paper introduces a framework for autonomously optimizing Agentic AI solutions across industries, such as NLP-driven enterprise applications. The system employs agents for Refinement, Execution, Evaluation, Modification, and Documentation, leveraging iterative feedback loops powered by an LLM (Llama 3.2-3B). The framework achieves optimal performance without human input by autonomously generating and testing hypotheses to improve system configurations. This approach enhances scalability and adaptability, offering a robust solution for real-world applications in dynamic environments. Case studies across diverse domains illustrate the transformative impact of this framework, showcasing significant improvements in output quality, relevance, and actionability. All data for these case studies, including original and evolved agent codes, along with their outputs, are here: https://anonymous.4open.science/r/evolver-1D11/

Paper Structure

This paper contains 31 sections, 8 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Architecture
  4. Synthesis Framework
  5. Evaluation Framework
  6. Refinement Process
  7. Case Studies
  8. Market Research Agent
  9. Medical AI Architect Agent
  10. Career Transition Agent
  11. Outreach Agent
  12. LinkedIn Agent
  13. Meeting Agent
  14. Lead Generation Agent
  15. Evaluation Results
  16. ...and 16 more sections

Figures (8)

  • Figure 1: Market Research Agent Refinement
  • Figure 2: AI Architect Agent Refinement
  • Figure 3: Career Transition Agent Refinement
  • Figure 4: Outreach Agent Refinement
  • Figure 5: LinkedIn Agent Refinement
  • ...and 3 more figures