TECH NEWS

Introducing the next generation of AWS Resilience Hub for generative AI-based SRE resilience journey | Amazon Web Services

Today, we’re announcing the next generation of AWS Resilience Hub with a significantly expanded experience that brings together a new application model, dependency discovery assessment, generative AI-powered failure mode analysis, modular resilience policies, and organization-wide reporting.

Organizations running hundreds of applications share a common challenge: availability is a top concern, yet there is no consistent way to set resilience goals, measure progress, or prove compliance across a portfolio. Teams set different standards, use different tools, and struggle to exchange information about whether applications actually meet expectations.

The next generation of AWS Resilience Hub changes this by giving Site Reliability Engineers (SREs) and development teams a structured way to align on resilience policy expectations, help application teams achieve them, and demonstrate compliance through testing. With integration into AWS Organizations, teams can now evaluate resilience at scale, identify failure modes, discover hidden dependencies, and report on progress across the enterprise.

...

Copyright of this story solely belongs to amazon.com. To see the full text click HERE

https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/28/20403.png

Evaluating Deep Agents using LangSmith on AWS | Amazon Web Services

This post was co-authored with Karan Singh, Head of Partnerships at LangChain Validating AI agent behavior before production is one of the hardest problems in applied AI. Agents are non-deterministic, multi-step where errors in early steps can affect downstream results. A single bad tool call can cascade through an entire

https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/28/19491.png

Streamline external access to Amazon SageMaker MLflow using a REST API proxy | Amazon Web Services

Machine learning (ML) teams use MLflow to manage their ML lifecycle effectively. Amazon SageMaker MLflow provides comprehensive ML experiment tracking and model management capabilities. However, many enterprises have existing infrastructure requirements that need HTTPS-based integrations rather than direct SDK usage. Many organizations need to integrate Amazon SageMaker MLflow with their

https://image.theregister.com/5248082.jpg?imageId=5248082&x=0&y=0&cropw=100&croph=100&panox=0&panoy=0&panow=100&panoh=100&width=1200&height=683

Snowflake buys Natoma to help freeze out rogue agents

It's 8 pm. Do you know where your agents are? Snowflake plans to buy Natoma, a startup that has made a gateway for managing AI agent permissions across enterprise applications, so users can focus on getting work done without wondering if their agents have violated security policies. During

https://image.theregister.com/254471.jpg?imageId=254471&x=0&y=0&cropw=100&croph=100&panox=0&panoy=0&panow=100&panoh=100&width=1200&height=683

Microsoft 0-day feud escalates as researcher threatens another Windows exploit dump

The ongoing saga of Microsoft versus Nightmare Eclipse (aka Chaotic Eclipse), the disgruntled bug hunter with a deep understanding of Windows and an even deeper grudge against Microsoft, reached a fever pitch, with the researcher, who has thus far released six Windows zero-days, promising a “bone shattering” drop on July

Read more

Evaluating Deep Agents using LangSmith on AWS | Amazon Web Services

Streamline external access to Amazon SageMaker MLflow using a REST API proxy | Amazon Web Services

Snowflake buys Natoma to help freeze out rogue agents

Microsoft 0-day feud escalates as researcher threatens another Windows exploit dump