IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI
  Systems
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
679191d8400d620e9c3e5eef
Elad Levi
admin_assigned
2025-01-23T09:49:58.917Z
635d618494e5b275ca73b844
/avatars/8cdaac6591a12b252612b99094e00959.svg
Levi
user
Eladlev
679191d8400d620e9c3e5ef0
Ilan Kadar
admin_assigned
2025-01-23T09:50:10.342Z
63ffa9c7e7767a895339ad5d
/avatars/479c605201e398abfd76f495ff448cd6.svg
Kadar
user
Ilankad23
IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI
  Systems
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
IntellAgent is a scalable, open-source multi-agent framework that uses policy-driven graph modeling and realistic event generation to comprehensively evaluate conversational AI systems in diverse, synthetic benchmarks.
LLMs Large Language Models conversational AI systems multi-turn dialogues domain-specific APIs IntellAgent scalable framework open-source synthetic benchmarks policy-driven graph modeling realistic event generation interactive user-agent simulations graph-based policy model performance gaps modular design reproducibility community collaboration