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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.
To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. 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.
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.
Enterprise data by its very nature spans diverse data domains, such as security, finance, product, and HR. Although this technology promises simplicity and ease of use for data access, converting natural language queries to complex database queries with accuracy and at enterprise scale has remained a significant challenge.
When used stand-alone, it cannot deliver the basic must-have requirements for enterprise use and above all, is not even designed for them. Unclear ROI ChatGPT is currently not accessible via API and the cost of a (hypythetical) API call are unclear. Its not as automated as people assume.
Amazon Q Business , a new generative AI-powered assistant, can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in an enterprises systems. In this post, we propose an end-to-end solution using Amazon Q Business to simplify integration of enterprise knowledge bases at scale.
The landscape of enterprise application development is undergoing a seismic shift with the advent of generative AI. Agent Creator is a versatile extension to the SnapLogic platform that is compatible with modern databases, APIs, and even legacy mainframe systems, fostering seamless integration across various data environments.
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.
However, even in a decentralized model, often LOBs must align with central governance controls and obtain approvals from the CCoE team for production deployment, adhering to global enterprise standards for areas such as access policies, model risk management, data privacy, and compliance posture, which can introduce governance complexities.
The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint. Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. A Business or Enterprise Google Workspace account with access to Google Chat.
Each drone follows predefined routes, with flight waypoints, altitude, and speed configured through an AWS API, using coordinates stored in Amazon DynamoDB. API Gateway plays a complementary role by acting as the main entry point for external applications, dashboards, and enterprise integrations.
These models offer enterprises a range of capabilities, balancing accuracy, speed, and cost-efficiency. Using its enterprise software, FloTorch conducted an extensive comparison between Amazon Nova models and OpenAIs GPT-4o models with the Comprehensive Retrieval Augmented Generation (CRAG) benchmark dataset.
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.
To enable the video insights solution, the architecture uses a combination of AWS services, including the following: Amazon API Gateway is a fully managed service that makes it straightforward for developers to create, publish, maintain, monitor, and secure APIs at scale.
Many enterprise customers across various industries are looking to adopt Generative AI to drive innovation, user productivity, and enhance customer experience. Amazon Q Business understands natural language and allows users to receive immediate, permissions-aware responses from enterprise data sources with citations.
Amazon Q Business is a fully managed, generative AI-powered assistant designed to enhance enterprise operations. Whether you’re a small startup or a large enterprise, this solution can help you maximize the potential of your Gmail data and empower your team with actionable insights. Choose Enable to enable this API.
Their results speak for themselvesAdobe achieved a 20-fold scale-up in model training while maintaining the enterprise-grade performance and reliability their customers expect. ServiceNows innovative AI solutions showcase their vision for enterprise-specific AI optimization. times lower latency compared to other platforms.
Note that these APIs use objects as namespaces, alleviating the need for explicit imports. API Gateway supports multiple mechanisms for controlling and managing access to an API. AWS Lambda handles the REST API integration, processing the requests and invoking the appropriate AWS services.
Heres what some of our AMs had to say about their experience with the account plans draft assistant: The AI assistant saved me at least 15 hours on my latest enterprise account plan. Enterprise Account Manager As someone managing multiple mid-market accounts, I struggled to create in-depth plans for all my customers.
This includes setting up Amazon API Gateway , AWS Lambda functions, and Amazon Athena to enable querying the structured sales data. Navigate to the AWS Secrets Manager console and find the secret -api-keys. Import the API schema from the openapi_schema.json file that you downloaded earlier. Download all three sample data files.
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.
For healthcare organizations, financial institutions, and enterprises handling confidential information, these risks can result in regulatory compliance violations and breach of customer trust. For more information, see Redacting PII entities with asynchronous jobs (API). The entities to mask can be configured using RedactionConfig.
With the rise of powerful foundation models (FMs) powered by services such as Amazon Bedrock and Amazon SageMaker JumpStart , enterprises want to exercise granular control over which users and groups can access and use these models. We provide code examples tailored to common enterprise governance scenarios.
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. Its used by the weather_agent() function.
GraphStorm is a low-code enterprise graph machine learning (GML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop.
These new features streamline the ML workflow by combining the convenience of pre-built solutions with the flexibility of custom development, while maintaining enterprise-grade security and governance. For enterprise customers, the ability to curate and fine-tune both pre-built and custom models is crucial for successful AI implementation.
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.
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.
Amazon Q Business is a conversational assistant powered by generative artificial intelligence (AI) that enhances workforce productivity by answering questions and completing tasks based on information in your enterprise systems, which each user is authorized to access.
Reduced time and effort in testing and deploying AI workflows with SDK APIs and serverless infrastructure. We can also quickly integrate flows with our applications using the SDK APIs for serverless flow execution — without wasting time in deployment and infrastructure management.
This blog post discusses how BMC Software added AWS Generative AI capabilities to its product BMC AMI zAdviser Enterprise. BMC AMI zAdviser Enterprise provides a wide range of DevOps KPIs to optimize mainframe development and enable teams to proactvely identify and resolve issues.
These steps might involve both the use of an LLM and external data sources and APIs. Agent plugin controller This component is responsible for the API integration to external data sources and APIs. The LLM agent is an orchestrator of a set of steps that might be necessary to complete the desired request.
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. By adopting this holistic evaluation approach, enterprises can fully harness the transformative power of generative AI applications.
Building proofs of concept is relatively straightforward because cutting-edge foundation models are available from specialized providers through a simple API call. Additionally, enterprises must ensure data security when handling proprietary and sensitive data, such as personal data or intellectual property. Who has access to the data?
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.
Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. We will start by using the SageMaker Studio UI and then by using APIs.
You can get started without any prior machine learning (ML) experience, and Amazon Personalize allows you to use APIs to build sophisticated personalization capabilities. After the model is trained, you can get the top recommended movies for each user by querying the recommender with each user ID through the Amazon Personalize Runtime API.
The technical sessions covering generative AI are divided into six areas: First, we’ll spotlight Amazon Q , the generative AI-powered assistant transforming software development and enterprise data utilization. Learn how Toyota utilizes analytics to detect emerging themes and unlock insights used by leaders across the enterprise.
We use various AWS services to deploy a complete solution that you can use to interact with an API providing real-time weather information. We also use identity pool to provide temporary AWS credentials for the user while they interact with Amazon Bedrock API. In this solution, we use Amazon Bedrock Agents.
Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API. Test the code using the native inference API for Anthropics Claude The following code uses the native inference API to send a text message to Anthropics Claude. client = boto3.client("bedrock-runtime",
With the general availability of Amazon Bedrock Agents , you can rapidly develop generative AI applications to run multi-step tasks across a myriad of enterprise systems and data sources. The embedding model, which is hosted on the same EC2 instance as the local LLM API inference server, converts the text chunks into vector representations.
Scalability The solution can handle multiple reviews simultaneously, making it suitable for organizations of all sizes, from startups to enterprises. Your data remains in the AWS Region where the API call is processed. Brijesh Pati is an Enterprise Solutions Architect at AWS, helping enterprise customers adopt cloud technologies.
To build a generative AI -based conversational application integrated with relevant data sources, an enterprise needs to invest time, money, and people. Alation is a data intelligence company serving more than 600 global enterprises, including 40% of the Fortune 100. This blog post is co-written with Gene Arnold from Alation.
Generative AI is revolutionizing enterprise automation, enabling AI systems to understand context, make decisions, and act independently. 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.
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