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AWS Resilience Hub adds three new capabilities

The next generation of AWS Resilience Hub is a central location in the AWS that helps platform engineering and site reliability teams assess and strengthen the resilience of their workloads running on AWS. It provides automated dependency discovery, generative AI-powered failure mode analysis, modular resilience policies, resilience testing, and organization-wide resilience posture reporting. Today, AWS Resilience Hub adds three new capabilities: EKS labels support for service input sources, dependency insights, and resilience policy sharing through AWS Organizations.
EKS labels support for service input sources. Resilience Hub now supports EKS labels within a namespace as a service input source, allowing customers to use their existing Kubernetes labeling conventions to scope exactly which resources Resilience Hub discovers and uses during failure mode analysis. This ensures assessments stay aligned with how teams organize their EKS workloads.
Dependency insights. Customers who enable dependency discovery can now generate dependency insights, a new generative AI-powered capability that analyzes discovered application dependencies and highlights meaningful patterns. Dependency insights surfaces new dependencies, identifies cross-Region dependencies, and flags unusual usage patterns. This helps teams accelerate dependency analysis and identify potential new risks.
Resilience policy sharing through AWS Organizations. Resilience Hub now supports sharing resilience policies through AWS Organizations, enabling central teams to build and manage policies that can be applied across multiple accounts. This provides organization-wide observability into which services are using each policy, making it easier to monitor adoption and ensure consistent resilience posture across the organization.
To get started, visit the  AWS console. To learn more, see the product page or visit the documentation.
Quelle: aws.amazon.com

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The new AgentCore Runtime is now available in Amazon Bedrock AgentCore

Today, AWS announces the availability of the next generation of AgentCore Runtime, the serverless microVM compute within Amazon Bedrock AgentCore. The new Runtime delivers elastic memory management that reclaims unused memory throughout the session so you pay for actual usage rather than the peak, and consistent cold start times regardless of container image size or concurrency. You get the serverless model you already rely on: no pre-provisioning, scale to zero, hardware-enforced session isolation, and pay only for what you use – now with lower costs and faster starts.
With the new Runtime, each session starts with a small, efficient memory profile. Additional memory is allocated on demand as the workload needs it, and memory that is no longer actively used is reclaimed rather than held until the session ends. For cold starts, the new Runtime prepares the agent environment once and snapshots it. Every new instance restores from that snapshot instead of repeating the full startup sequence, keeping start times consistent regardless of image size. In testing, the new Runtime delivered a P75 cold start of 1.9 to 2.0 seconds for container images from 200 MB to 2 GB, compared to 5.4–30 seconds with V1.
The new AgentCore Runtime is available in the following regions: us-east-1, us-east-2, us-west-2, eu-west-1, and ap-northeast-1. To get started, set platformVersion to V2 when creating or updating a runtime.
To learn more, visit the AgentCore Runtime documentation or the AWS News Blog. For pricing details, visit AgentCore pricing.
Quelle: aws.amazon.com

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Kimi K3 by Moonshot AI is now generally available on Amazon Bedrock

Amazon Bedrock continues to expand its open weight model portfolio with the same security and governance that customers rely on. Today, Kimi K3 from Moonshot AI is generally available on Amazon Bedrock, giving you a powerful new option for coding and knowledge work.
According to Moonshot AI, Kimi K3 is its most capable model and the first open model to reach 2.8 trillion parameters. It combines native vision capabilities with a 1-million-token context window, making it well suited to long-running coding sessions across large repositories, multi-document analysis including scanned pages and screenshots, and extended agent workflows. Moonshot AI reports an approximate 2.5x improvement in scaling efficiency over Kimi K2. On Amazon Bedrock, Kimi K3 runs within the same security boundary as proprietary models, and the same controls for access, encryption, and auditing across your model portfolio. Kimi K3 is the first open weight model on Amazon Bedrock to support explicit prompt caching, helping reduce latency and input costs when reusing context across model calls.
Kimi K3 is available in all AWS Regions where Amazon Bedrock is available through cross-Region inferencing. To get started, visit the Amazon Bedrock Console. To learn more, see the Amazon Bedrock documentation and read the launch blog post.
Quelle: aws.amazon.com