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When ecommerce teams need faster time-to-market for AI-powered customer experiences, they face weeks of custom integration work that delays launches and increases security risks. Building and connecting a production-ready AI assistant typically requires custom API code for each client, container infrastructure management, and complex authentication. Amazon Bedrock AgentCore and Mistral AI Studio streamline this process. […]

When you deploy generative AI agents with Amazon Bedrock AgentCore as production API endpoints, you might want to enforce web application firewall policies, rate limiting, protection against common web threats, or audit controls via AWS WAF. AWS WAF integrates with Elastic Load Balancing Application Load Balancers (ALBs), Amazon CloudFront distributions, and Amazon API Gateway REST

As organizations expand AI adoption across their workforce, IT administrators need a scalable way to manage how AI applications are configured and used on employee devices. These applications include Claude Code, Claude Desktop, and OpenAI Codex. Users, meanwhile, can open approved applications and start working without manual setup. Jamf, trusted by more than 78,000 organizations

If you’ve been managing Amazon Quick legacy Topics alongside your datasets, you know the challenge: two assets that must stay perfectly synchronized, each with its own permissions, lineage, and versioning. Column synonyms drift. Calculated fields diverge. A rename in the dataset breaks the Legacy Topic silently. You can now use Amazon Quick to embed that

Business intelligence analysts routinely face the same challenge at the start of every analytics project: the data needed to answer a single business question lives across multiple tables. Sales transactions sit in one place, customer demographics and product attributes in another, while returns, forecasts, and operational metrics occupy still others. Until now, combining these tables

In Part 1 of this series, we introduced Amazon Quick Sight Multi-Dataset Relationships and covered the foundational concepts of dimensional modeling, best practices for designing clean data models, and a decision framework for when to use runtime joins versus pre-joined datasets. If you haven’t read Part 1 yet, we recommend starting there. In this post,

Note: The topics referenced throughout this document refer to the new Topics experience (not legacy Topics). For details on the differences, see Build a unified semantic layer across datasets with multi-dataset Topics in Amazon Quick. Most real-world business questions span multiple tables. A retailer who wants to understand net revenue by product category must draw

Amazon Quick is an AI-powered unified intelligence service that connects structured data and unstructured enterprise content so teams can explore, analyze, and act from one place. Amazon Quick Sight, the business intelligence (BI) capability within Amazon Quick, delivers interactive dashboards, natural language querying, pixel-perfect reports, machine learning (ML)-driven insights, and embedded analytics. Topics in Quick

Building an AI agent that edits images based on natural language requires an orchestration loop, tool routing, memory management, and a compute environment to run it all. Amazon Bedrock AgentCore harness handles that entire stack with configuration. You declare what the agent does, and the harness runs it in a stateful, isolated microVM with built-in

The effectiveness and accuracy of machine learning (ML) models decreases almost as soon as the training job finishes. Changes in consumer behavior, releases of new products, upgrades in sensor technology, and a shifting economic and political landscape are all examples of uncontrollable factors that change the patterns and probabilities the model learned during training. By