Artificial Intelligence
Category: AWS Secrets Manager
Reference your own AWS Secrets Manager secrets in Amazon Bedrock AgentCore Identity
Today, we’re excited to announce the ability to reference a secret in AWS Secrets Manager for AgentCore Identity, so you can reference your own preconfigured secret from Secrets Manager and retain full control over how it is managed. With this ability, you can extend your organization’s existing secrets governance processes to AgentCore. You can provide an existing, preconfigured AWS Secrets Manager secret to use with your credential provider resources. You retain full control over its encryption configuration, rotation, replication, tags, and resource policies, just as you would manage other secrets in Secrets Manager. You can also choose a secret from another AWS account within the same AWS Region, though cross-Region secret sharing isn’t supported. This also supports secrets brought in through AWS Secrets Manager external connectors, enabling integration with third-party secret managers.
How AutoScout24 built a Bot Factory to standardize AI agent development with Amazon Bedrock
In this post, we explore the architecture that AutoScout24 used to build their standardized AI development framework, enabling rapid deployment of secure and scalable AI agents.
Integrate Amazon Bedrock Agents with Slack
In this post, we present a solution to incorporate Amazon Bedrock Agents in your Slack workspace. We guide you through configuring a Slack workspace, deploying integration components in Amazon Web Services, and using this solution.
Create a generative AI assistant with Slack and Amazon Bedrock
Seamless integration of customer experience, collaboration tools, and relevant data is the foundation for delivering knowledge-based productivity gains. In this post, we show you how to integrate the popular Slack messaging service with AWS generative AI services to build a natural language assistant where business users can ask questions of an unstructured dataset.
Use Snowflake as a data source to train ML models with Amazon SageMaker
May 2023: This blog post has been updated to include a workflow that does not require building a custom container. Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. […]
Getting started with the Amazon Kendra SharePoint Online connector
Amazon Kendra is a highly accurate and easy-to-use enterprise search service powered by machine learning (ML). To get started with Amazon Kendra, we offer data source connectors to get your documents easily ingested and indexed. This post describes how to use Amazon Kendra’s SharePoint Online connector. To allow the connector to access your SharePoint Online […]





