AWS Machine Learning Blog

The Amazon SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on the cluster your ML team already operates. This post shows how to install and configure the add-on, connect from the browser and from VS Code over SSH-over-SSM, and move your team to OpenID Connect sign-in with Amazon Cognito.

nOps rebuilt its Clara FinOps AI agent on Amazon Bedrock AgentCore, replacing a self-managed Amazon EKS stack running LangChain and LangGraph. The move cut time-to-production by 75% (from 10-12 months to 4 months), improved response quality, and reduced operational overhead while keeping analytics governed through Databricks Lakehouse Metric Views.

In this post, you learn how Cohere Health built a multi-tenant agentic architecture on AgentCore using AgentCore Runtime’s secure MicroVM isolation, unified tool access through AgentCore Gateway, AgentCore Memory, and the Agent Skills open standard to rapidly scale policy digitization capabilities, while preserving transparency, version control, and human oversight.

TReNDS, a research center at Georgia State University, built an agentic AI pipeline on Amazon Bedrock and the open-source Strands Agents SDK that automatically investigates production errors in real time, reducing root-cause analysis from 15 to 30 minutes of manual work to under 60 seconds.

The AWS Generative AI Innovation Center built an automated system that uses constraint programming and custom tree search to determine, with mathematical certainty, when and how an NHL team clinches a playoff spot. The approach was validated against four full NHL seasons of officially published results.

Temporal policies in Amazon Bedrock AgentCore let you define stateful rules that evaluate authorization based on an agent's session history. Learn how to enforce workflow sequencing, prevent data fabrication, cap financial exposure, and require human approval for high-value actions.

Learn how to configure rate limits on Amazon Bedrock AgentCore gateway to enforce per-user and per-target traffic controls. Define request, token, and connection limits scoped by JWT claims or IAM identity to protect downstream models, tools, and agents from traffic spikes.

Learn about new capabilities in Amazon Bedrock AgentCore: temporal policies powered by Dogwood, a new open source policy language for AI agents, and rate limiting on the gateway. These features give you deterministic control over sequences of agent actions and cost ceilings that hold regardless of agent behavior.

As engineering teams adopt coding agents like Codex, leaders need visibility into adoption, consumption, and reliability. This post shows how to route Codex OpenTelemetry metrics through a local collector to Amazon CloudWatch for an AWS native view of usage by user, team, and cost center.

A regulated customer needed all Claude Code inference processed in a single AWS Region (London), not just in-geography. This post shows two ways to pin Claude Code on Amazon Bedrock to one Region: an application inference profile or the Mantle endpoint, paired with an IAM Region condition, plus how to verify compliance in AWS CloudTrail.

Learn how to run the full Amazon Bedrock Automated Reasoning policy lifecycle from your coding agent. A suite of open source Agent Skills builds, reviews, tests, debugs, deploys, and validates a custom policy end to end, turning a specialized console task into a repeatable engineering workflow.

PDI Technologies built PDI Brew, an agentic platform on AWS where non-technical employees describe a tool in plain English and receive a fully provisioned, multi-tenant web application in seconds. See how a pluggable planner and an AWS Lambda provisioning agent turn plain-English intent into governed, multi-tenant apps backed by Amazon Bedrock.

The Amazon SageMaker Python SDK v3 now exposes generative AI inference recommendations in Amazon SageMaker AI directly in your notebook. Benchmark an endpoint, generate data-driven deployment recommendations, and deploy the recommended configuration without leaving your notebook workflow.

Learn how LendingTree built a production multi-agent mortgage assistant on Amazon Bedrock. Three coordinated agents use LangGraph, the Model Context Protocol, and Amazon Nova models with built-in guardrails to deliver 24/7 personalized mortgage guidance while meeting strict financial-services compliance.

In this post, we'll explore how Mobileye deployed an AI support agentic solution on Amazon Bedrock AgentCore - from the support bottleneck that sparked the idea, through the proof of concept that validated it, to the hybrid architecture that bridges on-premises systems with AWS cloud services. This approach is relevant for enterprises struggling to scale AI Agents while maintaining enterprise grade governance and security standards.

AI agents on Amazon Bedrock AgentCore run in the cloud, but users' tools and files live on their laptops. Learn how to build a secure MCP bridge that lets a cloud-hosted agent call local MCP servers by tunneling signed messages over the existing WebSocket connection through a browser extension and Chrome native messaging, with no open ports or VPN required.

Amazon Bedrock AgentCore harness is now generally available. Learn how to add it as an agent step in n8n workflows using a new open-source community node, and build agents with persistent memory, real tools, code execution, and VPC isolation — all from the n8n editor with no infrastructure or agent code.

Today, we are introducing the general availability of Web Search on Amazon Bedrock. It is a server-side built-in tool that grounds model responses in current web knowledge. With Web Search, grounding becomes a native capability of Amazon Bedrock, with no third-party vendors to onboard, no external APIs to orchestrate, and no additional third party vendor security reviews to conduct. In this post, we walk through what Web Search on Amazon Bedrock is, why it matters, how to enable it using the OpenAI Responses API, and how to get started with the tool.

Extracting insights from dozens of websites by hand quickly becomes overwhelming. This post shows how to build an automated web insight extraction solution with Amazon Bedrock AgentCore Browser, Amazon Bedrock, Amazon OpenSearch Serverless, and AWS Lambda that monitors RSS feeds, renders pages reliably, and makes AI-extracted insights searchable.

Formula 1® partnered with AWS to build the Data Accelerator, using agentic AI on Amazon Bedrock AgentCore to transform its MarTech data platform. Learn how F1 cut data source onboarding from up to 8 weeks to about 40 minutes, automated schema evolution, and gained end-to-end observability across its fan-engagement data estate.

Amazon Bedrock now supports automatic Automated Reasoning policy refinement. The refinement engine diagnoses failing tests and proposes formal-logic fixes for rule issues and language issues, and you approve every change before it takes effect. This post walks through both refinement modes with complete API and console workflows.

Amazon Quick announces the Agentic Catalog Experience, an AI-powered workflow for data curators to discover upstream catalog assets in natural language and auto-create Datasets and Topics with inherited semantics. Now in preview for AWS Glue Data Catalog and Databricks Unity Catalog.

Moonshot AI's Kimi K3, a 2.8 trillion parameter open-weight MoE model, can be deployed on AWS via SageMaker HyperPod or EKS using p6-b300 instances and vLLM.

Yahoo implemented Amazon Bedrock to boost its Search Retargeting (SRT) capabilities, using generative AI for keyword expansion, achieving up to 600x improvement in expansion rates and 5x growth in addressable audience.

Learn how to build an inference meta-monitoring system for Amazon SageMaker AI endpoints using Amazon Quick. This governance layer sits above production ML inference pipelines to continuously track prediction and data quality, detect drift, integrate delayed ground truth, and surface automated performance dashboards.

OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock, along with explicit prompt caching that gives you precise control over which parts of your prompt are cached and reused. Learn how to get started, set up explicit caching, and migrate existing GPT workloads to reduce inference cost.

Amazon Bedrock Advanced Prompt Optimization optimizes your prompts for up to 5 models at once and compares original versus optimized performance across quality, latency, and cost. Migrate to a new model or improve your current one in minutes instead of weeks.

Amazon Bedrock AgentCore Identity now supports Private Key JWT client authentication for agents, enabling authentication to identity providers using a signed JWT instead of a shared secret. The private key stays in AWS KMS, while the public key is registered with the identity provider. This article explains the workflow, supported grant flows (M2M, OBO, user-delegated), and provides a step-by-step guide for configuration, including creating a KMS signing key, registering the public key, and setting up a credential provider. Also includes example CloudTrail events for auditing.

Learn how Amazon Bedrock AgentCore delivers autonomous, cross-system business intelligence through configuration rather than custom code. Using pre-built MCP server connectors, fine-grained access control, and persistent memory, enterprises can query multiple data sources with natural language while enforcing role-based boundaries automatically.

Learn how to build a no-code customer retention pipeline in Amazon Quick that detects at-risk customers from call transcripts and CSAT data, scores them by retention priority with a custom MCP Action, and generates personalized retention letters, reducing response time from days to minutes.