CoMAI: A Collaborative Multi-Agent Framework for Robust and Equitable Interview Evaluation

TL;DR

CoMAI is a multi-agent framework that enhances AI-driven interview evaluation by improving robustness, fairness, and interpretability through modular agents and collaborative assessment strategies.

Contribution

This paper introduces CoMAI, a novel multi-agent system with a modular architecture that outperforms traditional single-agent models in fairness, security, and evaluation accuracy.

Findings

Achieved 90.47% accuracy in assessments

Attained 83.33% recall in candidate evaluation

Secured 84.41% candidate satisfaction

Abstract

Ensuring robust and fair interview assessment remains a key challenge in AI-driven evaluation. This paper presents CoMAI, a general-purpose multi-agent interview framework designed for diverse assessment scenarios. In contrast to monolithic single-agent systems based on large language models (LLMs), CoMAI employs a modular task-decomposition architecture coordinated through a centralized finite-state machine. The system comprises four agents specialized in question generation, security, scoring, and summarization. These agents work collaboratively to provide multi-layered security defenses against prompt injection, support multidimensional evaluation with adaptive difficulty adjustment, and enable rubric-based structured scoring that reduces subjective bias. Experimental results demonstrate that CoMAI achieved 90.47% accuracy, 83.33% recall, and 84.41% candidate satisfaction. These results highlight CoMAI as a robust, fair, and interpretable paradigm for AI-driven interview assessment.