What makes this system 'production-grade'?

It features a modular architecture of twelve independent, production-focused components, a comprehensive observability stack (OpenTelemetry, Prometheus, audit logs), and built-in governance for secure and scalable operations.

What key challenges does this system address for AI deployments?

It solves common production issues like multi-tenancy, robust authentication, comprehensive observability, rate limiting, caching, and circuit breaking for AI agents, ensuring enterprise-grade reliability.

What is the Production-Grade Agentic System?

It's a robust, production-ready Model Context Protocol (MCP) server architecture with an integrated multi-agent system, designed for secure, scalable, and reliable AI application deployment.

Is the system suitable for multi-tenant environments?

Yes, it is built with multi-tenancy as a core feature, including tenant-scoped authorization, row-level security, and governance mechanisms for isolated and secure operations.

How does it support AI agents and data access?

The system includes a four-agent copilot (Planner, Retriever, Synthesizer, Critic) and securely exposes a heterogeneous data layer (Postgres, Elasticsearch, S3, vector DB) to these AI agents.

Production-Grade Agentic System

Name: Production-Grade Agentic System
Author: FareedKhan-dev

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Provides a robust, production-grade Model Context Protocol (MCP) server architecture with a multi-agent system, designed for secure and scalable operation.

Production-Grade Agentic System

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Provides a robust, production-grade Model Context Protocol (MCP) server architecture with a multi-agent system, designed for secure and scalable operation.

This repository serves as a comprehensive reference implementation for building a production-ready Model Context Protocol (MCP) server, integrating a sophisticated multi-agent system. It tackles common challenges in real-world deployments by offering solutions for multi-tenancy, authentication, observability, rate limiting, caching, and circuit breaking. The system securely exposes diverse data layers (Postgres, Elasticsearch, S3, vector DB) to AI agents, and includes a four-agent support copilot (Planner, Retriever, Synthesizer, Critic) to demonstrate end-to-end functionality. Its modular design with twelve independent components ensures readability, maintainability, and extensibility for critical enterprise applications.

主要功能

010 GitHub stars

02Exposes heterogeneous data layer (Postgres, Elasticsearch, S3, vector DB) to AI agents securely

03Integrated four-agent support copilot (Planner, Retriever, Synthesizer, Critic) for end-to-end use cases

04Comprehensive observability stack including OpenTelemetry traces, Prometheus metrics, and structured audit logs

05Multi-tenant, authenticated, observable, rate-limited, cached, circuit-broken, and governed MCP server

06Modular architecture comprising twelve independent, production-focused components for reliability and scalability

使用案例

01Deploying secure and scalable Model Context Protocol (MCP) servers in production environments

02Establishing a reference architecture for production-grade agentic applications with reliability, governance, and observability

03Building robust multi-agent systems that interact with diverse enterprise data sources