Artificial Intelligence

It’s our goal for AWS to be the most secure place to run any workload, and in support of that we’ve been deeply investing in security across our services since AWS’s inception more than two decades ago. Our AI services like Amazon Bedrock are built on this foundation and with the same focus. 

Frontier teams are not just using AI to code faster. They’re redesigning how software gets built. The result is 4.5x productivity gains, in some cases more than 10x.

Amazon Bedrock AgentCore payments is now generally available, enabling AI agents to autonomously transact at scale with built-in spending guardrails, protocol-agnostic payment orchestration, and production-ready observability.

Amazon Quick embedded chat brings a conversational AI interface into your web application. This post walks through customizing the embedded chat with container and SDK styling, branding removal, and a custom agent persona so it matches your brand’s look, feel, and voice.

Learn how to build a multi-agent document classification solution on Amazon Bedrock using the Strands Agents SDK. Three specialized agents combine textual analysis with Claude Haiku 4.5 and visual similarity search with Amazon Titan Multimodal Embeddings to accurately classify insurance documents such as policies and affidavits.

Learn how Jumio built a centralized, real-time feature store on AWS with Amazon SageMaker Feature Store, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Streams. The architecture delivers sub-100ms feature serving for fraud detection and saves approximately $120,000 annually.

In this post, we describe how AIDA works at a high level and how it helps address these challenges — grounding users in the right contracts, under the right legal context, and within the right access boundaries. Specifically, we explore how AIDA uses implicit and explicit filtering, along with metadata-enriched chunking in Amazon Bedrock Knowledge Bases, to dramatically improve contract search accuracy.

Learn how Axonius, a cybersecurity SaaS provider, used Amazon Bedrock AgentCore to deploy fully isolated, multi-tenant AI agents across hundreds of customer environments, without building custom compute isolation, authentication, or observability infrastructure from scratch.

NVIDIA Nemotron 3.5 Lightning, an open model built for high-volume agentic workloads, is now available in Amazon SageMaker JumpStart. This post shows how to deploy the 30B Mixture-of-Experts model (3B active), which delivers up to 4x higher throughput and up to 30% faster task completion for always-on agents.

Give an autonomous agent a wallet and spending guardrails so it can pay for paywalled APIs, MCP servers, and web content. This post connects OpenClaw to Amazon Bedrock AgentCore payments and the x402 protocol, using the aws-agents-pay plugin to make bounded, human-approved testnet payments.

In multi-turn reinforcement learning, your custom reward function decides what the model actually learns. This post shows how to design a composite multi-turn reward for Amazon Nova Forge, execute model-generated code safely inside it, and instrument each component to catch the pitfalls that quietly collapse a reward.

Learn how to combine OpenAI-compatible endpoints on Amazon SageMaker AI with Amazon Bedrock AgentCore runtime to build a multi-agent workflow where each specialized agent uses the model best suited to its job. This post also shows how to get token-level observability from SageMaker endpoints that Strands Agents does not instrument by default.