Optimize agent tool selection using Amazon S3 Vectors and Amazon Bedrock Knowledge Bases

This article demonstrates how Amazon S3 Vectors and Amazon Bedrock Knowledge Bases optimize AI agent tool selection through semantic search, comparing vector-based retrieval against traditional baseline approaches.

• S3 Vectors enables cost-effective vector storage at scale with 90% lower costs than specialized databases
• Vector-based tool selection achieved 82.3% accuracy versus 75.8% baseline accuracy
• Semantic search reduced LLM inference costs by over 92% per query ($0.015 vs $0.202)
• End-to-end latency improved 21% faster with vector retrieval (4.25s vs 5.41s)
• Evaluation used MCPVerse benchmark with 422 real-world tools from Model Context Protocol servers
• S3 Vectors supports 2 billion vectors per index with strong consistency and 100ms query latency
• Total vector database cost for 1 million monthly queries: $2.57

S3 Vectors provides a practical, cost-effective solution for building custom agentic systems with intelligent tool selection capabilities.