Artificial Intelligence

Safely Releasing Frontier Models to Customers

Safely Releasing Frontier Models to Customers

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. 

Deploy a multimodal WhatsApp ordering assistant with Amazon Bedrock AgentCore

Deploy a multimodal WhatsApp ordering assistant with Amazon Bedrock AgentCore

Learn how to deploy a multimodal WhatsApp ordering assistant that takes customer orders through text, voice notes, and real-time voice calls on a single business number, built on Amazon Bedrock AgentCore with Amazon Nova 2. The channel and ordering layers stay separate, and one shared memory recognizes each customer across all three channels.

Designing memory lifecycle policies for Amazon Bedrock AgentCore

Designing lifecycle policies for AgentCore memory

Long-running AI agents accumulate outdated memories that degrade quality and create compliance risk. Learn how to design memory lifecycle policies for Amazon Bedrock AgentCore: scoring, consolidating, and pruning agent memories on a nightly AWS Step Functions workflow, with a deployable AWS CDK stack.

Build a Physical AI model factory with NVIDIA Cosmos 3 on SageMaker HyperPod

Build a Physical AI model factory with NVIDIA Cosmos 3 on SageMaker HyperPod

Building a Physical AI system takes a continuous pipeline, not a single training job. This post shows how to run that model factory (synthetic data generation, post-training, and closed-loop evaluation with NVIDIA Cosmos 3) on a persistent, resilient Amazon SageMaker HyperPod cluster on Amazon EKS, with GPU goodput as the metric that matters.

Run agent-driven Amazon SageMaker HyperPod operations with InstantStart

Run agent-driven Amazon SageMaker HyperPod operations with InstantStart

HyperPod InstantStart is an open source control plane that composes Amazon EKS orchestration with the managed capabilities of Amazon SageMaker HyperPod. It drives the same guarded operations through both a web interface and an AI agent, turning cluster bootstrap, capacity, training, inference, and storage into dependable, agent-driven infrastructure.

Customizing your knowledge base on Amazon Bedrock for large and complex documents using Amazon Textract

Customizing your knowledge base on Amazon Bedrock for large and complex documents using Amazon Textract

Learn how to customize an Amazon Bedrock knowledge base for large, complex documents by combining the high-accuracy text extraction of Amazon Textract with the generative AI of Amazon Bedrock. This post shows how to ingest and preprocess PDFs and images, then query utility bills at scale for faster, more accurate customer interactions.

How Intuit built an agentic disaster recovery assistant with Amazon Bedrock

How Intuit built an agentic disaster recovery assistant with Amazon Bedrock

Disaster recovery at scale is hard. Learn how Intuit built EWOK Agent, an agentic disaster recovery assistant on Amazon Bedrock that lets on-call engineers run production failovers from a plain-language request while keeping every action audited, policy-compliant, and safe.

AI-driven development lifecycle using Amazon Bedrock AgentCore

AI-driven development lifecycle using Amazon Bedrock AgentCore

Engineering teams adopting the AI-Driven Development Lifecycle (AI-DLC) often struggle to turn concepts into working code. This post walks through two reference implementations on Amazon Bedrock AgentCore, Kiro, and Claude Code: an SQL-to-ER-diagram generator and a multi-agent code security analyzer that put the AI-DLC construction phase into practice.

Migrate agentic workloads to Amazon Bedrock AgentCore

Migrate agentic workloads to Amazon Bedrock AgentCore

An agent that works in a notebook is not an agent in production. This post walks through migrating a LangGraph customer support agent to Amazon Bedrock AgentCore in two stages: onto Runtime, Gateway, and Memory, then to model-driven planning on Strands Agents, retiring operational burdens along the way.

Set up OpenAI ChatGPT Codex with LiteLLM on Amazon ECS and Amazon Bedrock

Set up OpenAI ChatGPT Codex with LiteLLM on Amazon ECS and Amazon Bedrock

Deploy a customer-operated LiteLLM gateway on Amazon ECS with AWS Fargate, connect it to an OpenAI model on Amazon Bedrock, and configure Codex to route requests through the gateway’s Responses API with scoped identities, budgets, rate limits, and telemetry. We also compare direct IAM Identity Center access and a managed Portkey deployment.