Enterprise AI & Workplace Automation ProtoMindSubmitted July 26, 2026

Agent-Sentinel

An MCP app on the Model Context Protocol built by ProtoMind at the Amrita University Coimbatore NitroStack × MCP To The Moon hackathon and deployed on NitroStack.

About this project

AgentSentinel is an AI-powered security platform that helps organizations discover, monitor, and secure AI agents operating across enterprise environments. As businesses increasingly adopt AI assistants, autonomous workflows, and AI-powered applications, many agents operate without proper visibility or governance, creating “Shadow AI” risks. AgentSentinel continuously scans enterprise systems to discover AI agents, builds a live inventory, evaluates their security posture, and assigns risk scores based on permissions, data access, behavior, and compliance policies. The platform detects suspicious activities such as unauthorized data access, excessive permissions, and abnormal agent behavior. When a high-risk agent is identified, AgentSentinel can automatically take protective actions such as quarantining the agent, revoking sensitive permissions, or notifying security teams. It also provides explainable AI-driven recommendations that help administrators understand why an agent was flagged and how to remediate the issue. Built using the Model Context Protocol (MCP) and NitroStack, AgentSentinel exposes security capabilities as MCP tools and resources, allowing enterprise AI systems to integrate seamlessly with existing workflows and security operations.

Enterprise AI & Workplace Automation track

Develop AI agents and automation tools that improve productivity, streamline workflows, and enhance business operations.

Source code

Team ProtoMind

  • S. SRI RAGHAV VATSANLead

  • Nethaaji S

  • Kavipranidan J T

  • Likhit Kella

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Frequently asked questions

What does Agent-Sentinel do?
AgentSentinel is an AI-powered security platform that helps organizations discover, monitor, and secure AI agents operating across enterprise environments. As businesses increasingly adopt AI assistants, autonomous workflows, and AI-powered applications, many agents operate without proper visibility or governance, creating “Shadow AI” risks. AgentSentinel continuously scans enterprise systems to discover AI agents, builds a live inventory, evaluates their security posture, and assigns risk scores based on permissions, data access, behavior, and compliance policies. The platform detects suspicious activities such as unauthorized data access, excessive permissions, and abnormal agent behavior. When a high-risk agent is identified, AgentSentinel can automatically take protective actions such as quarantining the agent, revoking sensitive permissions, or notifying security teams. It also provides explainable AI-driven recommendations that help administrators understand why an agent was flagged and how to remediate the issue. Built using the Model Context Protocol (MCP) and NitroStack, AgentSentinel exposes security capabilities as MCP tools and resources, allowing enterprise AI systems to integrate seamlessly with existing workflows and security operations.
Who built Agent-Sentinel?
Agent-Sentinel was built by team ProtoMind at the Amrita University Coimbatore NitroStack × MCP To The Moon hackathon, in the Enterprise AI & Workplace Automation track.
What is an MCP app and how is it built?
An MCP app is an application built on the Model Context Protocol — an open standard that lets AI agents connect to tools, data, and APIs. This project exposes MCP tools and resources that agentic AI systems can call. It was built and deployed on NitroStack, the full-stack platform for shipping MCP apps and servers.