Next-Gen Intelligent Agent Support Using Agentic AI

The primary objective of this project is to develop an enhanced iteration of BonBon by embedding intelligent conversation-routing capabilities that enable the transfer of user interactions to human experts once the AI encounters the boundaries of its functionality. This refinement directly responds to enduring limitations within AI-based support systems, particularly their insufficient capacity to address complex inquiries and their constrained access to dynamic, real-time information.

To realize this objective, the proposed solution employs a multi-agent architecture designed to enhance query resolution and ensure fluid collaboration between artificial and human agents. Drawing on the principles of multi-agent systems (MAS), the framework promotes effective communication and coordination across agents, thereby allowing AI components to process data more efficiently, extract meaningful insights, and contribute to evidence-based decision-making. The architecture is further supported by tools hosted on a dedicated MCP server, which provides advanced interoperability and facilitates seamless integration among heterogeneous AI agents.