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

**Authors:** Elad Levi, Ilan Kadar  
**arXiv:** [2501.11067](https://arxiv.org/abs/2501.11067)  
**Date:** 2025-01-19  
**Summary page:** https://www.summarizepaper.com/en/paper/2501.11067v1/

## Key points

- Large Language Models (LLMs) are evolving into task-oriented systems capable of autonomous planning and execution in artificial intelligence.
- Conversational AI systems utilizing LLMs must navigate multi-turn dialogues, integrate domain-specific APIs, and adhere to strict policy constraints.
- IntellAgent is introduced as a scalable, open-source multi-agent framework designed for comprehensive evaluation of conversational AI systems.
- IntellAgent automates the creation of diverse synthetic benchmarks through policy-driven graph modeling, realistic event generation, and interactive user-agent simulations.
- The framework simulates realistic multi-policy scenarios across varying complexity levels using a graph-based policy model for detailed diagnostics and optimization insights.
- IntellAgent emphasizes reliability for deploying conversational agents in high-stakes environments where policy violations can undermine trust.
- Results show a strong correlation between model performance on IntellAgent benchmarks and existing benchmarks like τ-bench despite relying entirely on synthetic data.
- Model performance decreases with increasing complexity levels but varies significantly across models, highlighting the framework's ability to provide detailed diagnostic insights for optimizing agent configurations based on specific requirements.

## Summary

Large Language Models (LLMs) are revolutionizing artificial intelligence by evolving into task-oriented systems capable of autonomous planning and execution. One of the key applications of LLMs is in conversational AI systems, which must navigate multi-turn dialogues, integrate domain-specific APIs, and adhere to strict policy constraints. However, evaluating these agents poses a significant challenge due to the complexity and variability of real-world interactions. To address this challenge, IntellAgent is introduced as a scalable, open-source multi-agent framework designed specifically for evaluating conversational AI systems comprehensively. By automating the creation of diverse synthetic benchmarks through policy-driven graph modeling, realistic event generation, and interactive user-agent simulations, IntellAgent offers fine-grained diagnostics that surpass the limitations of traditional static 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. It utilizes a graph-based policy model to capture the nuanced interplay of agent capabilities and policy constraints, providing highly detailed diagnostics that identify critical performance gaps for targeted optimization. The modular, open-source design supports seamless integration of new domains, policies, and APIs for reproducibility and community collaboration. The system diagram illustrates how IntellAgent generates events targeting subsets of policies based on chatbot prompts and Schema DB inputs. Each event triggers a simulated conversation between the user and chatbot to assess performance comprehensively. Reliability is emphasized as crucial for deploying conversational agents in high-stakes environments where policy violations can undermine trust. The study demonstrates IntellAgent's effectiveness as a benchmarking tool for evaluating conversational AI agents across diverse scenarios. Results show a strong correlation between model performance on IntellAgent benchmarks and existing benchmarks like τ-bench despite relying entirely on synthetic data. Key findings reveal that model performance decreases with increasing complexity levels but varies significantly across models, highlighting the framework's ability to provide detailed diagnostic insights for optimizing agent configurations based on specific requirements. In conclusion, IntellAgent contributes a scalable multi-agent evaluation framework that overcomes limitations of small-scale benchmarks to enable comprehensive evaluations of conversational AI agents' strengths and weaknesses. Its automated process generates diverse scenarios tailored to address unique challenges in evaluating agent performance thoroughly.