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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning

In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences. An automotive retailer might use inventory management APIs to track stock levels and catalog APIs for vehicle compatibility and specifications.

APIs 122
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Automate building guardrails for Amazon Bedrock using test-driven development

AWS Machine Learning

Clone the repo To get started, clone the repository by running the following command, and then switch to the working directory: git clone [link] Build your guardrail To build the guardrail, you can use the CreateGuardrail API. There are multiple components to a guardrail for Amazon Bedrock.

APIs 107
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Getting started with computer use in Amazon Bedrock Agents

AWS Machine Learning

Traditional automation approaches require custom API integrations for each application, creating significant development overhead. Add the Amazon Bedrock Agents supported computer use action groups to your agent using CreateAgentActionGroup API. Prerequisites AWS Command Line Interface (CLI), follow instructions here.

APIs 126
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Record a Call in Ruby with Vonage Voice API WebSockets

Nexmo

The Vonage Voice API WebSockets feature recently left Beta status and became generally available. Vonage API Account. To complete this tutorial, you will need a Vonage API account. Once you have an account, you can find your API Key and API Secret at the top of the Vonage API Dashboard. Building the Server.

APIs 125
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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

Using SageMaker with MLflow to track experiments The fully managed MLflow capability on SageMaker is built around three core components: MLflow tracking server This component can be quickly set up through the Amazon SageMaker Studio interface or using the API for more granular configurations.

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Orchestrate an intelligent document processing workflow using tools in Amazon Bedrock

AWS Machine Learning

The solution uses the FMs tool use capabilities, accessed through the Amazon Bedrock Converse API. This enables the FMs to not just process text, but to actively engage with various external tools and APIs to perform complex document analysis tasks. For more details on how tool use works, refer to The complete tool use workflow.

APIs 83
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Using natural language in Amazon Q Business: From searching and creating ServiceNow incidents and knowledge articles to generating insights

AWS Machine Learning

This involves creating an OAuth API endpoint in ServiceNow and using the web experience URL from Amazon Q Business as the callback URL. The final step of the solution involves enhancing the application environment with a custom plugin for ServiceNow using APIs defined in an OpenAPI schema.

APIs 77