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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.
Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Building generative AI applications requires more than model API calls.
However, as use cases have matured, the ability for a model to have access to tools or structures that would be inherently outside of the models frame of reference has become paramount. This could be APIs, code functions, or schemas and structures required by your end application. Amazon Nova will use the weather tool.
We demonstrate how generative AI along with external tool use offers a more flexible and adaptable solution to this challenge. The solution uses the FMs tool use capabilities, accessed through the Amazon Bedrock Converse API. For more details on how tool use works, refer to The complete tool use workflow.
Many businesses want to integrate these cutting-edge AI capabilities with their existing collaboration tools, such as Google Chat, to enhance productivity and decision-making processes. The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint.
In addition, they use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide an answer to the users request. This differs from confirmation flows where the agent directly executes API calls.
By using the power of LLMs and combining them with specialized tools and APIs, agents can tackle complex, multistep tasks that were previously beyond the reach of traditional AI systems. Whenever local database information is unavailable, it triggers an online search using the Tavily API.
It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.
This integration brings Anthropics visual perception capabilities as a managed tool within Amazon Bedrock Agents, providing you with a secure, traceable, and managed way to implement computer use automation in your workflows. Add the Amazon Bedrock Agents supported computer use action groups to your agent using CreateAgentActionGroup API.
By harnessing the latest advancements in generative AI, we empower employees to unlock new levels of efficiency and creativity within the tools they already use every day. Note that these APIs use objects as namespaces, alleviating the need for explicit imports. If required, this setup can also be defined in Infrastructure as Code.
Professionals in a wide variety of industries have adopted digital video conferencing tools as part of their regular meetings with suppliers, colleagues, and customers. For integration between services, we use API Gateway as an event trigger for our Lambda function, and DynamoDB as a highly scalable database to store our customer details.
Intricate workflows that require dynamic and complex API orchestration can often be complex to manage. In this post, we explore how chaining domain-specific agents using Amazon Bedrock Agents can transform a system of complex API interactions into streamlined, adaptive workflows, empowering your business to operate with agility and precision.
Amazon Bedrock Flows offers an intuitive visual builder and a set of APIs to seamlessly link foundation models (FMs), Amazon Bedrock features, and AWS services to build and automate user-defined generative AI workflows at scale. Test the flow Youre now ready to test the flow through the Amazon Bedrock console or API.
To help improve this process, in October 2024 we launched an AI-powered account planning draft assistant for our sales teams, building on the success of Field Advisor , an internal sales assistant tool. This new capability uses Amazon Bedrock to help our sales teams create comprehensive and insightful APs in less time.
SageMaker Unified Studio setup SageMaker Unified Studio is a browser-based web application where you can use all your data and tools for analytics and AI. Building a generative AI application SageMaker Unified Studio offers tools to discover and build with generative AI. To get started, you need to build a project.
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.
Gmail for business is part of Google Workspace , which provides a set of productivity and collaboration tools like Google Drive , Gmail , and Google Calendar. This tool aims to make employees work smarter, move faster, and drive more significant impact by providing immediate and relevant information and streamlining tasks.
The solution also uses Amazon Cognito user pools and identity pools for managing authentication and authorization of users, Amazon API Gateway REST APIs, AWS Lambda functions, and an Amazon Simple Storage Service (Amazon S3) bucket. To launch the solution in a different Region, change the aws_region parameter accordingly.
At AWS, we help our customers transform responsible AI from theory into practice—by giving them the tools, guidance, and resources to get started with purpose-built services and features, such as Amazon Bedrock Guardrails. These dimensions make up the foundation for developing and deploying AI applications in a responsible and safe manner.
AI-driven tools improve efficiency, accuracy, and scalability, enabling companies to handle multilingual interactions seamlessly. This article delves into the top 10 AI tools that are essential for enhancing multilingual customer support in 2025, providing insights into their functionalities, benefits, and implementation strategies.
Personalized outbound communication can be a powerful tool to increase user engagement and conversion. You can get started without any prior machine learning (ML) experience, and Amazon Personalize allows you to use APIs to build sophisticated personalization capabilities. In this example, we use Anthropics Claude 3.7
This approach, which we call intelligent metadata filtering, uses tool use (also known as function calling ) to dynamically extract metadata filters from natural language queries. Function calling allows LLMs to interact with external tools or functions, enhancing their ability to process and respond to complex queries.
Evaluations are also a fundamental tool during application development to validate the quality of prompt templates. It functions as a standalone HTTP server that provides various REST API endpoints for monitoring, recording, and visualizing experiment runs. This allows you to keep track of your ML experiments.
Agents for Bedrock are a game changer, allowing LLMs to complete complex tasks based on your own data and APIs, privately, securely, with setup in minutes (no training or fine tuning required). Amazon Bedrock is the first fully managed generative AI service to offer Llama 2, Meta’s next-generation LLM, through a managed API.
It allows you to configure the endpoints so they can scale to zero instances during periods of inactivity, providing an additional tool for resource management. You can retrieve the number of copies of an inference component at any time by making the DescribeInferenceComponent API call and checking the CurrentCopyCount.
The user’s request is sent to AWS API Gateway , which triggers a Lambda function to interact with Amazon Bedrock using Anthropic’s Claude Instant V1 FM to process the user’s request and generate a natural language response of the place location. It will then return the place name with the highest similarity score.
By choosing View API , you can also access the model using code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs. For more information on generating JSON using the Converse API, refer to Generating JSON with the Amazon Bedrock Converse API. Additionally, Pixtral Large supports the Converse API and tool usage.
Cloud providers have recognized the need to offer model inference through an API call, significantly streamlining the implementation of AI within applications. Although a single API call can address simple use cases, more complex ones may necessitate the use of multiple calls and integrations with other services.
For more information, see Redacting PII entities with asynchronous jobs (API). The query is then forwarded using a REST API call to an Amazon API Gateway endpoint along with the access tokens in the header. The user query is sent using an API call along with the authentication token through Amazon API Gateway.
Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.
Enabling Global Resiliency for an Amazon Lex bot is straightforward using the AWS Management Console , AWS Command Line Interface (AWS CLI), or APIs. Global Resiliency APIs Global Resiliency provides API support to create and manage replicas. To better understand the solution, refer to the following architecture diagram.
This tool enables marketers to craft compelling email subject lines that significantly boost open rates and engagement, tailored perfectly to the audience’s preferences and behaviors. To address these challenges, the organization developed an MLOps platform based on four key open-source tools: Airflow, Feast, dbt, and MLflow.
Agent Creator is a no-code visual tool that empowers business users and application developers to create sophisticated large language model (LLM) powered applications and agents without programming expertise. The robust capabilities and unified API of Amazon Bedrock make it an ideal foundation for developing enterprise-grade AI applications.
For more information about the SageMaker AI API, refer to the SageMaker AI API Reference. 8B-Instruct to DeepSeek-R1-Distill-Llama-8B, but the new model version has different API expectations. In this use case, you have configured a CloudWatch alarm to monitor for 4xx errors, which would indicate API compatibility issues.
Cropwise AI harnesses the power of generative AI using AWS to enhance Syngenta’s seed selection tools and streamline the decision-making process for farmers and sales representatives. The tool also streamlines data navigation, allowing users to efficiently explore and compare Syngenta’s extensive seed catalogue.
This blog post delves into how these innovative tools synergize to elevate the performance of your AI applications, ensuring they not only meet but exceed the exacting standards of enterprise-level deployments. Lets dive in and discover how these powerful tools can help you build more effective and reliable AI-powered solutions.
Besides the common AI functionalities like text and image generation, it allows them to interact with internal data, tools, and workflows through natural language queries. The implementation uses Slacks event subscription API to process incoming messages and Slacks Web API to send responses.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
MLflow , a popular open-source tool, helps data scientists organize, track, and analyze ML and generative AI experiments, making it easier to reproduce and compare results. SageMaker is a comprehensive, fully managed ML service designed to provide data scientists and ML engineers with the tools they need to handle the entire ML workflow.
This strategy equipped us to align each task with the most suitable foundation model (FM) and tools. Its equipped with the appropriate FM for the task and the necessary tools to perform actions and access knowledge. ToolsTools extend agent capabilities beyond the FM.
During these live events, F1 IT engineers must triage critical issues across its services, such as network degradation to one of its APIs. This impacts downstream services that consume data from the API, including products such as F1 TV, which offer live and on-demand coverage of every race as well as real-time telemetry.
This includes how we configured data sources that comprise our knowledge base, indexing documents and relevancy tuning , security (authentication, authorization, and guardrails ), and Amazon Qs APIs for conversation management and custom plugins. Jonathan Garcia is a Sr.
We took all of that feedback from customers, and today we are excited to announce Amazon Bedrock , a new service that makes FMs from AI21 Labs, Anthropic, Stability AI, and Amazon accessible via an API. They also spend a lot of time trying to keep up with a complex and ever-changing tool and technology landscape.
Whether you’re just starting your journey or well on your way, leave this talk with the knowledge and tools to unlock the transformative power of AI for customer interactions, the agent experience, and more. Then, explore how Volkswagen used these tools to streamline a job role mapping project, saving thousands of hours.
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