Production Multi-Agent AI Platform
Build an enterprise-grade AI platform with agents, RAG, MCP and real-world deployment.
Design and build a complete multi-agent system with planning, tools, memory, guardrails and observability. Learn to create a production-ready AI platform from scratch and deploy it with Streamlit and cloud infrastructure.
Digital product · 12 months access · Non-refundable after purchase
AI Research Assistant
Complete Source Code
Clean and documented
Deployment Guide
Step-by-step process
Resume Points
Ready-to-use for your resume
Interview Questions
With detailed explanations
What You'll Learn
Gain practical experience building a production-ready AI system, from architecture to deployment.
- Design multi-agent architectures with planner, researcher, coder and reviewer roles
- Orchestrate agent state, routing and handoffs using LangGraph
- Build a RAG pipeline: loading, chunking, embeddings and vector retrieval
- Connect agents to external tools and data through MCP servers
- Apply input/output guardrails and evaluate agent responses
- Add observability, tracing and robust error handling
- Build an interactive Streamlit interface with session state
- Containerize with Docker and deploy the platform to the cloud
- Document the project on GitHub and write strong resume points
- Explain the architecture and design trade-offs in interviews
Technologies Used
- Python
- LangGraph
- OpenAI
- MCP
- Streamlit
- Chroma / Vector DB
- Docker
- Cloud Deployment
Project Architecture
Understand how the complete system components work together.
Client
Application Layer
Orchestration
Specialist Agents
Tools & Knowledge
Safety & Ops
Project Demo
See the final application in action.
Preview of the final application you'll build.
- 5 Lessons · ~1 Hour
- 1. What is a Multi-Agent System?15 min
- 2. Real-World Use Cases8 min
- 3. Project Overview15 min
- 4. Final Architecture Walkthrough8 min
- 5. How to Use This Project15 min