Amazon S3 Vectors

2026-11-02

COMPANIES · 1 SOURCE · AWS Machine Learning

Amazon showcases agentic engineering assistant using Bedrock AgentCore and Nova Multimodal Embeddings

AWS published a how-to demonstrating a multi-agent engineering assistant built on Amazon Bedrock AgentCore that uses Amazon Nova Multimodal Embeddings, Claude 5.5-family models, Strands Agents SDK, and Amazon S3 Vectors to retrieve multimodal datasheets, score candidate parts with deterministic tolerance rules, compare photos, and draft audit-ready substitution assessments while keeping the engineer as decision-maker. The solution also shows managed knowledge bases, a web-search connector for live data, and generation of Integration Impact Assessments aligned with AS9100.

6.0

2026-10-14

CODING · 1 SOURCE · AWS Machine Learning

Build agentic intelligent document processing on Amazon Bedrock AgentCore with Strands Agents

AWS published a how-to for an agentic intelligent document processing (IDP) solution that uses Amazon Bedrock AgentCore and the open-source Strands Agents SDK to classify, extract, validate, and self-learn from business documents. The architecture ties AgentCore runtime and identity capabilities to AWS services (Textract, S3 Vectors, Aurora DSQL, Lambda, EventBridge, DynamoDB, ECS) in multi-agent workflows so new document formats can be processed and reused with minimal manual template configuration.

6.0

2026-10-01

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS documents Amazon Bedrock AgentCore multi-agent workflows and integrations for cloud migrations and agent workloads

An AWS technical post explains how AWS Professional Services uses a multi-agent framework built on Amazon Bedrock AgentCore to automate end-to-end enterprise cloud migrations and reduce infrastructure-as-code development time from weeks to minutes. The post also covers related how-tos and integrations — Live Data in Amazon QuickSight, using Amazon S3 Vectors as persistent memory with NVIDIA NeMo Agent Toolkit on EKS, ambient/ambient-agent patterns, Claude Platform configuration on AWS, personalization with SageMaker AI, fine-tuning Nova 2 Lite for uniopen, a Bedrock Knowledge Bases conversational claims assistant, and AgentCore Runtime Instances for colocated GPU agent workflows.

7.0

2026-10-01

CODING · 1 SOURCE · AWS Machine Learning

How to use Amazon S3 Vectors as persistent memory for NVIDIA NeMo Agent Toolkit (NAT v1.6)

A how-to showing how to implement Amazon S3 Vectors as a custom memory provider for the open-source NVIDIA NeMo Agent Toolkit (NAT), including a MemoryEditor plugin, YAML configuration, and deployment on Amazon EKS; the post was tested with NAT v1.6 and uses Amazon Titan Text Embeddings V2 for embeddings. It demonstrates mapping NAT's memory model (MemoryItem/MemoryEditor) to S3 Vectors indexes and metadata, and explains required tooling and configuration for a multi-agent investment research example.

5.0

2026-09-17

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS outlines vector-store choices for Amazon Bedrock Knowledge Bases (OpenSearch, Aurora pgvector, S3 Vectors)

AWS published guidance for the customer-managed configuration of Amazon Bedrock Knowledge Bases, comparing three supported vector-store backends—Amazon OpenSearch Service (managed and serverless), Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors—and evaluating their tradeoffs across RAG use cases (latency, cost, scalability). The post maps each backend to example workloads such as e-commerce product search and discusses performance and cost implications.

6.0