Amazon Quicksight Updates & Release Notes

We’re now in Preview for Glue Data Catalog (S3 data) & Databricks Unity Catalog Integration!

For all customers with Glue Data Catalog with S3 data using Athena (or Databricks Unity Catalog), the Agentic Catalog Experience can be used. To try this, customers can go to Create Data Source (on the structured data side), select Glue Data Catalog (or Databricks) and hit ‘Explore data’

What our customers get:

• Agentic Catalog Experience for GDC and UC — Discover & create catalog representations (Quick Datasets/Topics) using natural language/inherit semantics and relationships. 3 benefits of taking this catalog approach over a direct MCP connection:

  1. Data teams can create curated context boundaries for their structured data - grounded Q&A (more accurate answers)
  2. Deterministic dashboards and all other Quick use cases (Q&A and others)
  3. Unified context in Quick - by having catalog representations (as Datasets and Topics) in Quick, we form the complete context (structured data + Slack + Outlook and other sources) to the end users

YouTube video captures these benefits

Resources:

• YouTube: Preview announcement video
• LinkedIn announcement
• Full documentation

Go ahead and try the feature out, today! If there are any questions, let us know by responding to this thread.

• Amazon Quick Team.

Today, AWS announces multiple new features for Amazon Quick, including autonomous agents, multi-dataset analytics capabilities, and a redesigned activity feed. Amazon Quick is the AI assistant that connects to popular business applications and learns user workflows. These new capabilities enable Quick to handle recurring tasks continuously while providing unified analytics across multiple data sources.

With autonomous agents, users can describe tasks in natural language and set granular autonomy levels—from step-by-step approval to broad goal-based execution. Agents operate continuously to automate workflows like following up on stalled deals, summarizing regulatory changes, and processing purchase orders, eliminating manual repetitive work and notification overload. The new multi-dataset analytics feature enables users to query across data sources including Snowflake and relational databases using natural language, without requiring technical data preparation or pre-joining datasets. Quick inherits semantic intelligence from existing data catalogs such as AWS Glue, Databricks Unity Catalog, and Collibra, while enforcing security through identity propagation that respects existing permissions.

The redesigned activity feed provides a personalized, conversational interface where users can prioritize updates using thumbs up/down feedback, reply to emails and Slack messages, and approve requests directly—all without switching between applications. Users can also share Quick applications as public websites, extending collaboration capabilities beyond their organization.

To learn more about these new Amazon Quick capabilities, including autonomous agents, multi-dataset analytics., and redesigned activity feed, read the launch blog. You can create an account for free and get started in minutes at aws.com/quick.

Amazon Quick now integrates with Snowflake Cortex AI through the Model Context Protocol (MCP), enabling teams to query their Snowflake data and documents using natural language, and automate multi-step workflows directly within their Quick workspace. After setting up the connection using Snowflake’s managed MCP server with OAuth authentication, you can ask questions across structured data through Cortex Analyst and retrieve insights from unstructured documents through Cortex Search.

With this integration, you can build Flows in Quick that orchestrate Snowflake Cortex Agents to execute repeatable, governed workflows with consistent structured output. This is ideal for any multi-step process that spans structured data and unstructured documents. The same MCP connection is also accessible from Quick Chat and other Quick features. For example, users can ask ad-hoc follow-up questions or explore their Snowflake data conversationally alongside their automated flows. Quick intelligently routes relevant prompts to Snowflake Cortex AI and returns contextualized answers alongside enterprise knowledge stored in Quick Spaces, giving teams both the rigor of a structured process and the flexibility of a conversational interface.

The Snowflake Cortex AI integration with Amazon Quick is available in all AWS Regions where Amazon Quick is available.

Visit the Amazon Quick website to learn more and start your Quick free trial. To learn more about the Snowflake Cortex AI integration, read the blog. To learn more about Quick integrations, visit the integrations page.

Amazon SageMaker Studio quick setup now completes in under twenty seconds, reduced from over two minutes. Whether you are building ML pipelines, exploring data, developing with notebooks, or fine-tuning foundation models, you can go from sign-in to a fully configured Studio environment almost instantly.

As part of this streamlined setup, newly created Studio environments now come with serverless model customization permissions automatically configured. A new managed policy, AmazonSageMakerModelCustomizationCoreAccess, is created and attached for you, providing permissions for serverless model customization jobs including fine-tuning with custom reward functions for reinforcement learning, model evaluation, and deployment to SageMaker or Bedrock endpoints. This eliminates the need to manually create and configure IAM roles and policies before you can start experimenting. For existing Studio environments, actionable messages with direct links to documentation guide you through adding these permissions.

This feature is available in all AWS Commercial Regions where Amazon SageMaker Studio is supported. To get started, create a new Studio environment using quick setup in the SageMaker AI Console. To learn more, see Quick setup and Model Customization permissions setup in the Amazon SageMaker documentation.

Amazon Quick now enables enterprise customers to connect their privately hosted Model Context Protocol (MCP) servers to Quick through Amazon Virtual Private Cloud (VPC). Amazon Quick is an AI assistant that turns questions into answers, answers into actions, and actions into outcomes for you and your entire team. Previously, Quick’s MCP support was limited to third-party hosted servers accessible over the public internet. With VPC support, organizations that host MCP servers on private networks for proprietary applications, custom data sources, and internal tools can now securely extend those capabilities to AI workflows in Quick.

With VPC connectivity for MCP, you can connect Quick to MCP servers running on Amazon EC2, AWS Fargate, AWS Agentcore, or other compute within your private network without exposing them to the internet. During MCP connector creation, select your VPC connection and provide your MCP server URL. Once connected, your team interacts with private MCP servers through natural language in Quick, with all traffic routed securely through your VPC.

VPC support for MCP servers is available in all AWS Regions where Amazon Quick is available.

Learn more about Amazon Quick and try for free. To learn more about connecting private MCP servers, visit the MCP documentation and the VPC connectivity guide.

Amazon Quick Research now enables customers to encrypt their data using customer-managed keys (CMK) through AWS Key Management Service (KMS).

This enhancement allows organizations with strict security and compliance requirements to manage their own encryption keys. With customer-managed keys, you gain enhanced security control and comprehensive audit capabilities through AWS CloudTrail integration. You can encrypt your data with your own KMS keys, trace all data access for security auditing, and revoke access to compromised keys within 15 minutes during security incidents. This feature supports multiple CMKs with one default key per AWS account per region, providing the flexibility to manage encryption across different datasets while maintaining granular control over your sensitive business intelligence data.

Customer-managed keys must be created in the same AWS account and region as your Quick resources, and only symmetric AWS KMS keys are supported.

This feature is generally available in all AWS Regions where Amazon Quick is available. To learn more, visit the Amazon Quick Research detail page.

Today, Amazon Quick is announcing cross-account access for Amazon Athena data sources. This launch enables you to query Athena data residing in a different AWS account(s) from your Quick deployment using IAM role chaining, with Athena query costs billed to the account where the data lives.

With this feature, administrators can create an Athena data source in Quick by specifying a RunAsRole in the Quick account and a ConsumerAccountRoleArn in the target account where Athena resources reside. Quick uses a role chaining mechanism first assuming the RunAsRole, then chaining into the consumer account role to execute queries. This launch supports multiple roles per consumer account(s), enabling fine-grained access segregation across teams within a single account.

This feature is now available in all supported Amazon Quick Sight regions here. For more details, read our blog post.

This is a companion discussion topic for the original entry at https://aws.amazon.com/about-aws/whats-new/2026/05/amazon-quick-athena/

Today, AWS announces the general availability of the AWS MCP Server, a managed server that gives AI coding agents secure, auditable access to AWS services through the Model Context Protocol (MCP). The AWS MCP Server is a core component of the Agent Toolkit for AWS, which helps coding agents build on AWS more effectively. With the AWS MCP Server, organizations can let coding agents interact with AWS while maintaining visibility and control through IAM-based guardrails, Amazon CloudWatch metrics, and AWS CloudTrail logging.

Since the preview launch at re:Invent 2025, the AWS MCP Server has added several capabilities. Agents can now call any AWS API through a single tool, including operations that require file uploads or long-running execution. Sandboxed script execution lets agents run Python code against AWS services for multi-step operations, without access to your local filesystem or shell tools. Agent skills replace agent SOPs with a more flexible format: agents discover and load curated guidance on demand, keeping context window usage low while providing tested procedures for complex tasks. Additionally, documentation search and skill discovery no longer require AWS credentials, removing a common barrier to getting started.

The AWS MCP Server is available at no additional charge; you pay only for the AWS resources your agents use. To learn more, see Agent Toolkit for AWS. To get started, visit the Agent Toolkit for AWS Quick Start guide.

You can use the AWS MCP Server in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt).

This is a companion discussion topic for the original entry at https://aws.amazon.com/about-aws/whats-new/2026/05/aws-mcp-server

Today, AWS is launching the Agent Toolkit for AWS, a production-ready suite of tools and guidance that helps AI coding agents build on AWS with fewer errors, lower token costs, and enterprise-grade security controls. The Agent Toolkit for AWS is the successor to the MCP servers, plugins, and skills available on AWS Labs.

Developers using coding agents to build on AWS often find that their agents struggle with complex multi-service workflows, rely on outdated knowledge of AWS services, and are difficult to govern — leading to wasted time, wasted tokens, and a reluctance to deploy agents in production. The Agent Toolkit for AWS addresses these challenges through agent skills, a fully-managed MCP server, and easy-to-install plugins. Agent skills give agents validated, up-to-date procedures for tasks like authoring CloudFormation templates, configuring data pipelines, and building serverless applications — so agents follow best practices rather than improvising from general knowledge. Today, we are launching more than 40 skills across infrastructure-as-code, storage, analytics, serverless, containers, and AI services, and we plan to release more in the coming weeks: including for databases, networking, and IAM. Each skill has been rigorously evaluated to ensure that it helps agents complete tasks more accurately and reliably. The AWS MCP Server, now generally available, is a fully-managed MCP server that allows coding agents to interact with any AWS service. It offers IAM-based guardrails on which actions agents can perform, Amazon CloudWatch and AWS CloudTrail observability, and sandboxed code execution for multi-step operations. The AWS MCP server also equips agents with tools to efficiently search and retrieve documentation, so they always have the latest knowledge and guidance. Agent plugins bundle the AWS MCP server and curated sets of skills into a single install. Today, we are releasing three agent plugins: AWS Core, to help application developers build and manage full-stack applications on AWS, AWS Data Analytics, which helps data analysts and business intelligence engineers create data pipelines and load and query data, and AWS Agents, which helps AI engineers build production-ready agents using Amazon Bedrock AgentCore.

The MCP servers, skills, and plugins available on AWS Labs will continue to be available, and over time the best of AWS Labs will be transitioned to the Agent Toolkit for AWS to ensure that customers can access the broadest array of tooling and guidance for their agents. The Agent Toolkit for AWS is available at no additional charge; you pay only for the AWS resources your agents use. To learn more, see Agent Toolkit for AWS. To get started, visit the Quick Start guide or browse the available skills and plugins on GitHub.

You can use the AWS MCP Server in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt).

Amazon Quick, your AI assistant for work, now integrates with New Relic’s AI agents, enabling on-call engineers, SREs, and engineering leaders to investigate incidents, generate root cause analysis briefs, and create tracked tasks without leaving their Amazon Quick workspace.

After connecting to New Relic’s remote model context protocol (MCP) server, you can invoke New Relic’s AI agents directly from a conversational prompt in Quick – including alert insights, user impact analysis, log analysis, transaction diagnostics, and natural language NRQL queries. In a single chat exchange, you can investigate an incident across your observability data, generate a root cause analysis (RCA) document with evidence links, and send it as an email attachment. Quick Flows can also invoke New Relic AI agents to automate recurring triage runbooks or escalation workflows. Because Quick surfaces responses alongside enterprise knowledge stored in Spaces - such as runbooks, architecture docs, and on-call policies—every answer reflects both live telemetry and organizational context.

The New Relic integration with Amazon Quick is available in all AWS Regions where Amazon Quick is available.

To get started with Amazon Quick, visit the website and sign up in minutes. To learn more about the New Relic integration, read the New Relic integration guide, and explore more Quick integrations on the integrations page.

Amazon Quick now generates dashboards from natural language prompts with Generate Analysis. You describe the dashboard you want, select up to three datasets, and review an editable plan before generation. Amazon Quick then produces organized sheets with visuals selected for your data, filter controls for exploring by different dimensions, and calculated fields such as year-over-year growth and month-over-month comparisons. Generate Analysis reduces dashboard creation from hours of manual configuration to minutes.

With Generate Analysis, you can describe goals such as "create a sales performance dashboard with revenue trends, regional comparisons, and month-over-month growth" and receive a dashboard ready for refinement. The output works with existing publishing workflows, embedding, CI/CD pipelines, and point-and-click editing.

At launch, Generate Analysis is available to Enterprise subscription/Author Pro users. Authors also have promotional access to this capability through December 2026 as part of Amazon Quick Enterprise, provided their organization has not restricted access. Generate Analysis is now generally available in all AWS Regions where Amazon Quick is available.

To learn more, see Generating an analysis with natural language prompts in the Amazon Quick User Guide. To get started, open any dataset in Amazon Quick and choose Generate analysis.

AWS Transform customers can now use BI migration agents to convert Tableau and Power BI dashboards to Amazon Quick Sight (BI capability of Amazon Quick) assets, helping reduce migration effort from months to days.

These agents are built by Wavicle Data Solutions, an AWS Advanced Consulting Partner, leveraging the AWS Transform initiative to create differentiated transformation solutions by integrating specialized agents, tools, knowledge bases, and workflow with AWS Transform’s agentic AI capabilities.

Four agents are available for purchase through AWS Marketplace: one Analyzer agent and one Converter agent for each BI migration source (Power BI and Tableau).

AWS Transform

AWS Transform is a collaborative enterprise IT transformation workbench powered by expert agents, agentic AI systems, and continuous learning that accelerates cloud migration, legacy app modernization, and tech debt reduction.

These new BI migration agents are embedded into the AWS Transform workflow and use a chat-based interface to assess your source dashboards for migration readiness, then convert them – rebuilding datasets, calculated fields, visualizations, and filters in Amazon Quick Sight.

All processing runs within your AWS account; no data leaves your environment.

After conversion, your Amazon Quick administrators assign dashboard ownership to BI authors for validation and publishing.

Once migrated, your teams can take advantage of Amazon Quick's AI-powered workflows, including natural-language business questions, automated research, and data-driven actions.

Availability

The BI migration agents are available through AWS Marketplace in US East (N. Virginia).

They support Quick Sight asset creation in all commercial regions where Amazon Quick Sight is available.

To get started, subscribe through AWS Marketplace (Power BI or Tableau) or contact your AWS account team to explore available programs for free or discounted Amazon Quick migrations.

Read more in this blog post.

This is a companion discussion topic for the original entry at https://aws.amazon.com/about-aws/whats-new/2026/05/quick-bi-migration/

AWS Transform customers can now use BI migration agents to convert Tableau and Power BI dashboards to Amazon Quick Sight (BI capability of Amazon Quick) assets, helping reduce migration effort from months to days. These agents are built by Wavicle Data Solutions, an AWS Advanced Consulting Partner, leveraging the AWS Transform initiative to create differentiated transformation solutions by integrating specialized agents, tools, knowledge bases, and workflow with AWS Transform’s agentic AI capabilities. Four agents are available for purchase through AWS Marketplace: one Analyzer agent and one Converter agent for each BI migration source (Power BI and Tableau).

AWS Transform is a collaborative enterprise IT transformation workbench powered by expert agents, agentic AI systems, and continuous learning that accelerates cloud migration, legacy app modernization, and tech debt reduction. These new BI migration agents are embedded into the AWS Transform workflow and use a chat-based interface to assess your source dashboards for migration readiness, then convert them – rebuilding datasets, calculated fields, visualizations, and filters in Amazon Quick Sight. All processing runs within your AWS account; no data leaves your environment. After conversion, your Amazon Quick administrators assign dashboard ownership to BI authors for validation and publishing. Once migrated, your teams can take advantage of Amazon Quick’s AI-powered workflows, including natural-language business questions, automated research, and data-driven actions.

The BI migration agents are available through AWS Marketplace in US East (N. Virginia). They support Quick Sight asset creation in all commercial regions where Amazon Quick Sight is available. To get started, subscribe through AWS Marketplace (Power BI or Tableau) or contact your AWS account team to explore available programs for free or discounted Amazon Quick migrations. Read more in this blog post.

Create an AWS account

Today, Amazon Quick introduces new and upgraded Microsoft 365 extensions in preview for Excel, PowerPoint, and Word, enabling Quick to perform tasks directly within users’ Microsoft 365 environments.

These extensions allow you to use AI to perform complex local tasks such as redlining documents, building financial models, and creating presentation-ready decks.

The Microsoft Excel extension helps with complex spreadsheet analysis, creating pivot tables and charts, and importing and cleaning data. The Microsoft PowerPoint extension helps you create and refine presentations from Quick data using organization-defined templates. Updates to the Microsoft Word extension include the ability to generate formatted documents with Word primitives, make sweeping edits with track changes enabled, and participate as a reviewer in comments.

These extensions transform daily work across teams. Finance teams can build complex models by describing what they need, and sales teams can draft proposals that automatically pull from CRM data. Marketing teams can create branded presentations without manual formatting, legal teams can streamline contract reviews, and IT teams can automate routine data analysis that previously required manual effort.

Amazon Quick extensions are available in US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), Europe (Ireland), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (London).

Start working with Amazon Quick by signing up for an account. To learn more about Amazon Quick, visit the Quick website, and install extensions on the Quick download page.

Quick Sight in Amazon Quick now supports custom sort for filter controls, giving authors control over how values appear in dropdown and list controls. Previously, filter control values were always sorted alphabetically. With custom sort, authors can arrange values to match business logic or rank them by a related metric, so the most relevant options appear first.

Custom sort applies to dropdown and list controls, both single-select and multi-select. Authors can choose ascending, descending, or a fully user-defined order for controls with manually entered values. For controls tied to a dataset column, authors can sort by that column or by a different field using aggregation functions like Sum, Average, Count, Min, and Max. For example, a priority field can be ordered as Critical, High, Medium, Low instead of alphabetically, or a list of product categories can be ranked by total revenue so top sellers surface first.

This feature is now available in all Amazon Quick regions where Quick Sight is supported. Learn more about sorting filter control values in the Amazon Quick User Guide.

This is a companion discussion topic for the original entry at https://aws.amazon.com/about-aws/whats-new/2026/04/quick-filter-control-sort/