Lyzr Agent Studio faced significant challenges in scaling its AI agent infrastructure, initially relying on Weaviate and Pinecone for vector database management. As demand grew, these systems struggled with increased query latency, resource bottlenecks, and inefficiencies in handling over 100 concurrent agents and large data volumes. In response, Lyzr transitioned to Qdrant, which delivered a >90% reduction in query latency, faster indexing operations, and a 30% decrease in infrastructure costs, while maintaining stability under heavy loads. Qdrant's efficient horizontal scalability and low resource utilization enabled Lyzr to handle over 1,000 queries per minute and sustain high throughput across distributed agents. Use cases with NTT Data and NPD demonstrated improved retrieval accuracy and consistent performance, highlighting Qdrant's capability to meet production-grade demands and enhance AI agent performance significantly.