title: IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI
    Systems
summary: 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
paper_authors._id: 679191d8400d620e9c3e5eef
paper_authors.name: Elad Levi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-23T09:49:58.917Z
paper_authors._id: 635d618494e5b275ca73b844
paper_authors.avatarUrl: /avatars/8cdaac6591a12b252612b99094e00959.svg
paper_authors.fullname: Levi
paper_authors.type: user
paper_authors.user: Eladlev
paper_authors._id: 679191d8400d620e9c3e5ef0
paper_authors.name: Ilan Kadar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-23T09:50:10.342Z
paper_authors._id: 63ffa9c7e7767a895339ad5d
paper_authors.avatarUrl: /avatars/479c605201e398abfd76f495ff448cd6.svg
paper_authors.fullname: Kadar
paper_authors.type: user
paper_authors.user: Ilankad23
paper_title: IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI
    Systems
paper_summary: 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
paper_ai_keywords: sizable Language Models (LLMs) task-oriented systems conversational AI systems multi-turn dialogues domain-specific APIs policy constraints synthetic benchmarks policy-driven graph modeling realistic event generation user-agent simulations fine-grained diagnostics coarse-grained metrics realistic, multi-policy scenarios policy interactions graph-based policy model targeted optimization community collaboration