Multi-Agent Generative AI: Coordinated Synthesis for Complex Problem-Solving

Single generative models struggle to handle tasks requiring diverse reasoning and multi-domain knowledge. This study presents a multi-agent generative AI architecture where specialized models collaborate via a negotiation and task allocation protocol. Agents can generate partial outputs, critique peers, and synthesize final results using consensus-based scoring. The paper introduces a generative agent orchestration framework using LangGraph and Apache Kafka, enabling distributed reasoning across domains such as legal analysis, software engineering, and biomedical research. Experiments demonstrate improved factual consistency, reduced hallucinations, and enhanced output diversity compared to monolithic models. The framework achieves a 34% reduction in factual errors and 42% improvement in task completion rates across complex multi-domain problems. The research presents three novel contributions: a novel orchestration protocol for multi-agent generative collaboration, consensus-driven synthesis mechanisms to reduce factual errors, and real-time distributed generation pipelines across heterogeneous model types. Comprehensive evaluation across diverse problem domains validates the effectiveness of coordinated multi-agent approaches for complex reasoning tasks requiring specialized domain expertise and cross-disciplinary integration.