Build production-ready AI Agents with Qdrant and n8n

Learn how to design and deploy advanced AI agents with n8n’s low-code self-hosted AI starter kit and Qdrant’s vector database

Why? Because looking beyond RAG, AI agents are the next step in intelligent systems, in which LLMs serve as “brains of operation,” orchestrating tools and storing their “knowledge” in vector databases.

Vector databases "under the hood" of AI agents can be much more than a similarity search engine—they also can be used for diversity search, anomaly detection, and classification, especially useful for limited-label scenarios or expanding class structures over time.

It isn't easy to bring these impactful AI agents from ideation to production. You want:

• to make the LLM produce consistent output in the required format
• to seamlessly integrate tools with AI agents
• debug and manage agentic pipelines

all of which can be difficult. 

Watch this video to: 

Learn how to deploy advanced AI agents using Qdrant’s vector database power beyond similarity search and n8n’s low-code AI starter kit.

We’ll demonstrate how AI agents detect anomalies in satellite images (scaling search to the entire Earth) and moderate spam, showing how versatile they can be and how to overcome the challenge of making them production-ready.