DynamoDB Native Vector Search Gives Serverless AI Agents Durable Semantic Memory

Developers can add long-term semantic memory to serverless AI agents using DynamoDB's native vector search and Amazon Bedrock embeddings, eliminating the need for a dedicated vector database or sync pipeline. The approach stores embeddings alongside data items in a single DynamoDB table, so both a write and a vector index update happen in one PutItem call. Benchmarks run in AWS us-east-1 showed this native method was approximately 2.5x faster on writes and 1.4x faster on searches at p50 latency compared to S3 Vectors. The solution directly addresses a common AWS Lambda problem where container teardown between invocations causes each agent turn to start without any memory of prior conversations. The post also distinguishes three separate memory layers — state, session, and long-term memory — arguing that conflating them is a root cause of agents remembering the wrong things or forgetting the right ones.