AgentNexus: Accelerating AI Agent Development and Enhancing Interoperability with MCP
Name
MIT-LIN-151366.pdf
Description
Main Report
Size
4.51 MB
Format
Adobe PDF
Checksum (MD5)
b0bf430a3c01d475611695ee08e99d7a
Author(s)
Yae, Jung
Hamilton, Lei
Date Issued
February 17, 2026
Abstract
The DoD faces significant challenges in its pursuit
of AI superiority, as disparate data and development platforms
create redundant efforts and limit interoperability. Additionally,
existing DoD systems are ill-equipped to handle the recent
paradigm shift toward agentic AI, which requires modern standards
and tools. To address these gaps, this paper introduces
AgentNexus, an application designed to streamline the development,
deployment, and servicing of AI agents. AgentNexus
provides an application featuring an advanced agents processing
backend, a scalable service layer, and an intuitive user interface.
It provides pre-built toolkits, sophisticated RAG pipeline, and
MCP for enhanced interoperability. The successful development
of an Education Assistant agent validates the application’s capacity
to support the rapid implementation of multi-agent workflows.
By fostering a collaborative and standardized environment,
AgentNexus mitigates critical barriers of interoperability and
duplicated effort, accelerating the delivery of multi-agent AI to
warfighters.
Subjects
AI Agent, Document Intelligence, Education, Hybrid Search, MCP, Multi-agent
MIT Department
Lincoln Laboratory
Persistent DSpace Link
