IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems

2025-01-19

AI Summary

Conventional evaluation methods inadequately capture chatbots’ real-world capabilities in multi-turn dialogue, tool invocation, and policy compliance. Method: This paper introduces the first open-source, multi-agent framework for evaluating conversational AI systems. It innovatively employs *Policy Graphs* to explicitly model complex, multi-strategy interactions; integrates event-driven simulation, user-agent co-interactive protocols, and modular API interfaces to automatically generate diverse, scalable synthetic benchmarks. Contribution/Results: The framework overcomes limitations of static or manually curated benchmarks by enabling multi-level, joint diagnostics of both functional capabilities and behavioral constraints. Experiments demonstrate its effectiveness in identifying critical performance gaps—including policy adherence, multi-turn consistency, and API robustness—across multiple domains. Empirical validation confirms high diagnostic accuracy and strong cross-domain generalization capability.

Technology Category

Multiagent Systems: Adversarial AgentsNatural Language Processing: Conversational AI/Dialog SystemsHumans and AI: Human-Aware Planning and Behavior Prediction

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsEconomics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

Large Language Models (LLMs) are transforming artificial intelligence, evolving into task-oriented systems capable of autonomous planning and execution. One of the primary applications of LLMs is conversational AI systems, which must navigate multi-turn dialogues, integrate domain-specific APIs, and adhere to strict policy constraints. However, evaluating these agents remains a significant challenge, as traditional methods fail to capture the complexity and variability of real-world interactions. We introduce IntellAgent, a scalable, open-source multi-agent framework designed to evaluate conversational AI systems comprehensively. IntellAgent automates the creation of diverse, synthetic benchmarks by combining policy-driven graph modeling, realistic event generation, and interactive user-agent simulations. This innovative approach provides fine-grained diagnostics, addressing the limitations of static and manually curated benchmarks with coarse-grained metrics. IntellAgent represents a paradigm shift in evaluating conversational AI. By simulating realistic, multi-policy scenarios across varying levels of complexity, IntellAgent captures the nuanced interplay of agent capabilities and policy constraints. Unlike traditional methods, it employs a graph-based policy model to represent relationships, likelihoods, and complexities of policy interactions, enabling highly detailed diagnostics. IntellAgent also identifies critical performance gaps, offering actionable insights for targeted optimization. Its modular, open-source design supports seamless integration of new domains, policies, and APIs, fostering reproducibility and community collaboration. Our findings demonstrate that IntellAgent serves as an effective framework for advancing conversational AI by addressing challenges in bridging research and deployment. The framework is available at https://github.com/plurai-ai/intellagent

Problem

• Multi-turn Dialogue
• Tool Utilization
• Rule Compliance

Innovation

• IntellAgent
• Rule-based Interactions
• Chatbot Evaluation