An Interactive Multi-Agent System for Evaluation of New Product Concepts
arXiv:2603.05980v1 Announce Type: new Abstract: Product concept evaluation is a critical stage that determines strategic resource allocation and project success in enterprises. However, traditional expert-led approaches face limitations such as subjective bias and high time and cost requirements. To support this process, this study proposes an automated approach utilizing a large language model (LLM)-based multi-agent system (MAS). Through a systematic analysis of previous research on product development and team collaboration, this study established two primary evaluation dimensions, namely technical feasibility and market feasibility. The proposed system consists of a team of eight virtual agents representing specialized domains such as R&D and marketing. These agents use retrieval-augmented generation (RAG) and real-time search tools to gather objective evidence and validate concepts through structured deliberations based on the established criteria. The agents were further fine-tu
arXiv:2603.05980v1 Announce Type: new Abstract: Product concept evaluation is a critical stage that determines strategic resource allocation and project success in enterprises. However, traditional expert-led approaches face limitations such as subjective bias and high time and cost requirements. To support this process, this study proposes an automated approach utilizing a large language model (LLM)-based multi-agent system (MAS). Through a systematic analysis of previous research on product development and team collaboration, this study established two primary evaluation dimensions, namely technical feasibility and market feasibility. The proposed system consists of a team of eight virtual agents representing specialized domains such as R&D and marketing. These agents use retrieval-augmented generation (RAG) and real-time search tools to gather objective evidence and validate concepts through structured deliberations based on the established criteria. The agents were further fine-tuned using professional product review data to enhance their judgment accuracy. A case study involving professional display monitor concepts demonstrated that the system's evaluation rankings were consistent with those of senior industry experts. These results confirm the usability of the proposed multi-agent-based evaluation approach for supporting product development decisions.
Executive Summary
This article introduces an innovative, interactive multi-agent system (MAS) leveraging a large language model (LLM) to automate the evaluation of new product concepts. Traditional expert-driven evaluation methods are often constrained by subjective bias and resource inefficiency, and this study addresses these limitations by deploying a virtual agent network composed of eight domain-specific agents—such as R&D and marketing—that utilize retrieval-augmented generation (RAG) and real-time search tools to aggregate objective evidence and assess concepts against predefined criteria of technical and market feasibility. The agents are fine-tuned using professional product review data, enhancing their predictive accuracy. A case study with display monitor concepts demonstrated alignment with expert evaluations, validating the system’s utility. The research offers a scalable, objective alternative to conventional evaluation frameworks.
Key Points
- ▸ Deployment of LLM-based multi-agent system for product concept evaluation
- ▸ Utilization of retrieval-augmented generation and real-time search for objective evidence gathering
- ▸ Validation through case study demonstrating consistency with expert rankings
Merits
Objective Decision Support
The system reduces subjective bias by automating evaluation using structured criteria and external data validation.
Scalability and Efficiency
By leveraging AI agents and RAG, the system offers faster, cost-effective evaluation without requiring extensive human intervention.
Demerits
Dependence on Data Quality
Accuracy of agent judgments is contingent upon the quality, relevance, and representativeness of fine-tuning data—potential limitation if data is skewed or incomplete.
Expert Commentary
This paper presents a compelling and pragmatic application of AI in the domain of product innovation evaluation. The integration of domain-specific agents with retrieval-augmented generation represents a sophisticated evolution of AI-assisted decision support. Notably, the authors’ decision to anchor the evaluation framework on technical and market feasibility—two core pillars of product viability—provides a robust conceptual foundation. The case study validation is particularly convincing, as alignment with senior expert opinion is a strong indicator of predictive validity. However, the reliance on fine-tuning data warrants deeper scrutiny: without transparency on the dataset’s provenance, diversity, and bias mitigation, the system’s generalizability remains vulnerable. Moreover, the study does not address scalability beyond a single case study; future work should explore multi-industry applicability and integration with existing PLM or ERP systems. Overall, this work advances the field by offering a tangible, empirically validated AI-driven alternative to subjective evaluation, and it sets a precedent for future AI-augmented innovation management.
Recommendations
- ✓ 1. Organizations should pilot the MAS in controlled product development cycles to assess impact on decision quality and time-to-market.
- ✓ 2. Researchers should expand validation across diverse product categories and industries to confirm generalizability and identify contextual biases.