Cognitive Alignment in Multi-Agent Generative AI Systems: A Framework for Trustworthy Collaborative Intelligence
Ayushi Singh
The rapid evolution of generative artificial intelligence (AI) has led to the emergence of multi-agent systems capable of autonomous reasoning, collaboration, and decision-making. However, ensuring alignment among multiple AI agents and human intent remains a critical challenge. This paper introduces a novel concept termed Cognitive Alignment in Multi-Agent Generative Systems (CAMAGS), focusing on how multiple AI agents can maintain consistent reasoning, ethical alignment, and cooperative behavior. We propose a hybrid framework combining neuro-symbolic reasoning, alignment constraints, and self-reflective feedback loops. The study evaluates emerging risks such as hallucination propagation, agent conflict, and ethical drift. Results suggest that cognitive alignment mechanisms significantly improve trust, reliability, and scalability in collaborative AI ecosystems. Keywords: Artificial Intelligence, Multi-Agent Systems, Alignment, Generative AI, Trustworthy AI, Neuro-symbolic AI