IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems (2501.11067v1)

Abstract: 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

• The paper presents IntellAgent, a framework that automates conversational AI evaluation through synthetic scenarios and graph-based policy modeling.
• It scales evaluation by simulating diverse, multi-turn interactions and quantifying policy adherence across complex conversational tasks.
• The framework reveals strong performance correlations with real-world benchmarks, emphasizing the need for nuanced diagnostics in AI deployment.

IntellAgent: A Comprehensive Framework for Evaluating Conversational AI Systems

The paper introduces IntellAgent, an open-source, scalable framework designed to evaluate conversational AI systems, particularly those powered by LLMs. This framework addresses the limitations of traditional evaluation methods, which often fail to capture the intricate dynamics of multi-turn interactions and the complexities associated with policy adherence and tool integration in real-world applications.

Core Contribution

IntellAgent is designed to assess the performance of conversational agents by simulating diverse, synthetic scenarios using a novel approach that combines policy-driven graph modeling, event generation, and interactive simulations. The framework offers:

• Automation and Scalability: By leveraging automated techniques for synthetic data generation, IntellAgent creates diverse, complex scenarios across multiple dimensions. This automation mitigates the drawbacks of limited coverage and manual curation typically seen in traditional benchmarks.
• Graph-Based Policy Modeling: It employs a graph-based model to represent and manage relationships, complexities, and likelihoods of policy co-occurrences within conversations. This facilitates comprehensive, fine-grained diagnostics and highlights potential performance gaps.
• Open-Source and Modular Design: The modular architecture supports the seamless integration of new domains, policies, and APIs, while maintaining reproducibility and fostering community collaboration.

Evaluation and Findings

The framework was evaluated across different conversational AI models, revealing several insights:

• Performance Correlation: The evaluation showed a strong correlation between model performance on IntellAgent's synthetic benchmarks and existing real-world benchmarks like the $\tau$-bench, despite using entirely synthetic data.
• Complexity and Performance Decline: Models displayed varying degrees of performance decline as task complexity increased. This underscores the need for detailed diagnostics when choosing the appropriate model for specific applications.
• Policy-Specific Insights: IntellAgent uncovered significant variations in model performance across different policy categories, allowing for a nuanced understanding of models’ capabilities and weaknesses.

Implications and Future Directions

The development of IntellAgent has implications for both theoretical research and practical deployment of conversational AI systems. By providing a detailed and scalable assessment platform, it facilitates the optimization and fine-tuning of these agents, ensuring they meet the demands of complex, real-world applications.

Future work could explore integrating real-world user interactions within the evaluation framework to enhance the realism and applicability of the generated scenarios. This could further improve the quality of the policy graph, potentially leading to better weight assignments and more accurate performance insights.

In conclusion, IntellAgent offers a comprehensive and adaptable solution to the challenge of evaluating conversational AI systems, presenting a significant step towards more reliable and trustworthy AI deployment in various domains.