?? Join Our Cutting-Edge AI Security PoC Build: Implement mTLS for LangChain & LlamaIndex! ?? DM me to discuss participating.
George Polzer
Sr. Product Manager AI/ML | EU & US Go-to-Market / MVP Consultant | Emerging Tech - Agentic AI, Agent Ops Focus??
We're forming a collaborative team to prototype an mTLS-secured AI agent communication framework using LangChain and LlamaIndex. This will be an open PoC for AI developers, solution architects, and security engineers to experiment, contribute, and innovate together!
?? LangChain Agent (Agent LC): A simple AI assistant generating responses to text queries.
?? LlamaIndex Agent (Agent LI): A knowledge retrieval agent fetching info from a document database.
?? Here’s the challenge:
How do we ensure that only authorized AI agents can communicate and access sensitive data—without relying on traditional passwords or insecure API keys?
?? Here is the PoC -> MVP Roadmap:
● Set up a Certificate Authority for agent identity verification
● Deploy LangChain text generation agents with certificate-based authentication
● Implement LlamaIndex knowledge retrieval agents with secure database access
● Configure MongoDB with mTLS for secure document storage
● Create a complete end-to-end authenticated AI workflow
?? What Is Being Built / Work-in-Progress:
1?? LangChain Agent – A conversational AI agent to generate intelligent responses.
2?? LlamaIndex Agent – A document retrieval agent that fetches knowledge from a secure database.
3?? mTLS Security Layer – Both agents must mutually authenticate before exchanging data.
4?? Secure Database Access – Only authenticated AI agents can retrieve knowledge from a protected database.
??? Tech Stack
? FastAPI + Uvicorn – To create mTLS-enabled AI agents
? OpenSSL Foundation – For generating secure agent certificates
? MongoDB (or Flask-based DB) – Secure document knowledge base
? Docker/Kubernetes – To package and test the architecture efficiently
?? Why MongoDB? It offers robust mTLS support out-of-the-box, scales well for AI workloads, and provides flexible document storage perfect for LlamaIndex's vector embeddings.
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