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. 

From portal-hopping to instant answers: HEMA’s journey with MCP and Amazon Bedrock

From portal-hopping to instant answers: HEMA’s journey with MCP and Amazon Bedrock

HEMA, a 100-year-old Dutch retailer, turned developer portal-hopping into instant answers by building HAL, an internal AI assistant on Amazon Bedrock AgentCore. Using Model Context Protocol (MCP), HAL delivers governed knowledge inside the tools teams already use, with no AWS credentials on the client and security anchored in Microsoft Entra ID.

Agentic conversational video intelligence built on AWS

Agentic conversational video intelligence built on AWS

Learn how to build a conversational video intelligence solution on AWS using an agentic architecture. A single Strands Agents SDK agent orchestrates Amazon Bedrock, Amazon Rekognition, and Amazon Transcribe at runtime, deciding which service to call so you can ask natural language questions about your videos and get answers in seconds.

Use open weight models as your AI coding agent with Amazon Bedrock

Use open weight models as your AI coding agent with Amazon Bedrock

Pair OpenCode, an open-source terminal-native AI coding agent, with open weight models on Amazon Bedrock to get a secure, flexible, pay-per-use coding assistant. Learn how to configure multi-model workflows, match the right model to each task, and keep your data in your own AWS account with no infrastructure to manage.

Evaluate skill-equipped agents with Strands Evals and Amazon Bedrock AgentCore

Evaluate skill-equipped agents with Strands Evals and Amazon Bedrock AgentCore

Skills let you encode domain-specific procedures as reusable, portable instructions for agents, but a fluent answer doesn’t prove the agent picked the right skill or followed it. Learn how to measure skill selection and instruction following with Strands Evals and Amazon Bedrock AgentCore Evaluations.

Right-size generative AI endpoints with concurrency sweeps on Amazon SageMaker AI

Right-size generative AI endpoints with concurrency sweeps on Amazon SageMaker AI

Concurrency sweeps help you right-size a generative AI endpoint on Amazon SageMaker AI by systematically benchmarking it at increasing load levels. This post walks through deploying a model, running automated concurrency sweeps with the CreateAIBenchmarkJob API, and using the results to make data-driven capacity decisions about fleet size.

How Trane gets building insights 60x faster with Amazon Bedrock AgentCore

How Trane gets building insights 60x faster with Amazon Bedrock AgentCore

In about four weeks, Trane Technologies built an AI-powered agentic solution on Amazon Bedrock AgentCore that reduced a 20-minute, multi-screen building diagnostic workflow to a 20-second natural language interaction, a 60x improvement in time-to-insight. This post shares the architectural approach and key design decisions behind the solution.

How Tata Elxsi detects industrial safety risks in seconds on AWS

How Tata Elxsi detects industrial safety risks in seconds on AWS

Learn how Tata Elxsi built IRIS, a real-time industrial safety platform on AWS. IRIS filters camera video at the edge, streams metadata through Amazon Kinesis, runs computer vision on Amazon SageMaker AI, and correlates detections into high-confidence alerts, detecting unsafe conditions in seconds instead of minutes.