Multi-Agent Actor-Critic Generative AI for Query Resolution and Analysis

2025-02-17

AI Summary

This work addresses the challenge of ambiguous natural language queries hindering AI systems from generating actionable data insights. We propose MASQRAD, a novel multi-agent framework that introduces an Actor-Critic generative collaboration paradigm. It comprises three specialized agents—Actor (for query understanding and code generation), Critic (for iterative, debate-driven script refinement), and Expert (for semantic interpretation and visualization-guided analysis)—operating in a closed-loop to transform imprecise user requests into executable SQL/Python scripts, dynamic visualizations, and interpretable decision recommendations, all accessible via a unified interface. Leveraging domain-adaptive prompt engineering and reinforcement learning–based script optimization, MASQRAD achieves 87% accuracy on natural language–to-visualization tasks—significantly outperforming baseline methods—while reducing ambiguity-induced response errors and enhancing both analytical reliability and user decision-making efficiency.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

In this paper, we introduce MASQRAD (Multi-Agent Strategic Query Resolution and Diagnostic tool), a transformative framework for query resolution based on the actor-critic model, which utilizes multiple generative AI agents. MASQRAD is excellent at translating imprecise or ambiguous user inquiries into precise and actionable requests. This framework generates pertinent visualizations and responses to these focused queries, as well as thorough analyses and insightful interpretations for users. MASQRAD addresses the common shortcomings of existing solutions in domains that demand fast and precise data interpretation, such as their incapacity to successfully apply AI for generating actionable insights and their challenges with the inherent ambiguity of user queries. MASQRAD functions as a sophisticated multi-agent system but"masquerades"to users as a single AI entity, which lowers errors and enhances data interaction. This approach makes use of three primary AI agents: Actor Generative AI, Critic Generative AI, and Expert Analysis Generative AI. Each is crucial for creating, enhancing, and evaluating data interactions. The Actor AI generates Python scripts to generate data visualizations from large datasets within operational constraints, and the Critic AI rigorously refines these scripts through multi-agent debate. Finally, the Expert Analysis AI contextualizes the outcomes to aid in decision-making. With an accuracy rate of 87% when handling tasks related to natural language visualization, MASQRAD establishes new benchmarks for automated data interpretation and showcases a noteworthy advancement that has the potential to revolutionize AI-driven applications.

Problem

• Resolves ambiguous user queries precisely
• Generates actionable insights from data
• Enhances AI-driven data interpretation accuracy

Innovation

• Multi-Agent Actor-Critic model
• Generative AI for visualization
• Expert Analysis AI for decision-making