agentic

# Agentic Enterprise ## Description Agentic is a sample system designed to offer intelligent business services through an agent-based architecture. The project consists of multiple services working together using MCP (Model Control Protocol) to provide comprehensive solution. ## Architecture The system architecture is based on three main components that communicate with each other: - A central application agent that receives user requests - A RAG (Retrieval Augmented Generation) service to answer queries with contextual knowledge, exposed via MCP. - A monitoring service to provide system status information, exposed via MCP. ```mermaid flowchart TD subgraph application["application-agent (Port: 8080)"] app["🤖 Application Agent"] style app fill:#f0f0f0,stroke:#333,stroke-width:2px mc1["Monitor MCP Client"] mc2["RAG MCP Client"] app --- mc1 app --- mc2 style app font-weight:bold end subgraph system-status["Monitor Service (Port: 8081)"] status["🤖 System Status Service"] style status fill:#eef7e6,stroke:#333,stroke-width:2px status-server["Monitor MCP Server"] status --- status-server style status font-weight:bold end subgraph rag-service["RAG Service (Port: 8082)"] rag["🤖 RAG Agent"] style rag fill:#e6f7ff,stroke:#333,stroke-width:2px rag-server["Rag MCP Server"] kb["Redis Vector Store"] rag --- rag-server rag --- kb style rag font-weight:bold end %% Increased spacing between components using invisible nodes invisible1[" "] invisible2[" "] application ~~~ invisible1 ~~~ rag-service application ~~~ invisible2 ~~~ system-status %% Connect with longer edges mc1 --> rag-server mc2 --> status-server %% Hide invisible nodes style invisible1 fill:none,stroke:none style invisible2 fill:none,stroke:none classDef server fill:#f0f0f0,stroke:#666,stroke-width:1px class rag-server,status-server server classDef client fill:#f0f0f0,stroke:#666,stroke-width:1px class mc1,mc2 client classDef data fill:#f9f9f9,stroke:#999,stroke-width:1px class kb,api data ``` ## Modules ### application-agent Main agent that receives user requests, via rest endpoint, and orchestrates the other services. **Configuration**: - **Port**: 8080 - **Dependencies**: Connects to `rag` and `monitor` services via MCP clients **Configuration file**: [application.yml](application-agent/src/main/resources/application.yml) ### rag Retrieval Augmented Generation (RAG) agent that provides responses based on stored knowledge. Redis vector store is used for efficient indexing and retrieval of information. **Configuration**: - **Port**: 8081 - **Type**: MCP Server - **Storage**: Redis Vector Store for efficient indexing and retrieval **Configuration file**: [application.yml](rag/src/main/resources/application.yml) ### monitor **Role**: Monitoring service that provides information about the system status. **Configuration**: - **Port**: 8082 - **Type**: MCP Server - **Functionality**: Collects fict data about system health and performance **Configuration file**: [application.yml](monitor/src/main/resources/application.yml) ## Running the Project ### Redis ```bash cd environment; docker-compose up -d ``` ### Starting Services **1. Start the RAG service**: ```bash cd rag mvn spring-boot:run ``` **2. Start the monitor service**: ```bash cd monitor mvn spring-boot:run ``` **3. Start the application agent**: ```bash cd application-agent mvn spring-boot:run ``` ## Usage Send requests to the application agent on port 8080. The system will coordinate communication with the RAG and monitor services to retrieve the necessary information and respond accordingly. ## Video Audio is awful. Just watch the video without sound. 🙂 [](https://www.loom.com/embed/8f2e07544fa1403c9f6fb2e07c817ad0)