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Guidance for Multi-Agent Employee Virtual Assistant on AWS

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Overview

This Guidance demonstrates how TeamLink AI, an employee virtual assistant, centralizes access to cross-functional knowledge through a unified, intelligent chat interface. Leveraging advanced language models hosted on Amazon Bedrock, this virtual assistant helps break down departmental information silos by providing employees with instant access to critical organizational knowledge. This Guidance streamlines workplace efficiency by helping employees quickly find and retrieve the information they need, when they need it, eliminating the traditional barriers between different departmental knowledge bases.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

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Deploy with confidence

Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs. 

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Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Lambda orchestrates interactions between services, while DynamoDB tracks conversation history. Amazon Bedrock manages AI capabilities, andAmazon OpenSearch Serviceefficiently indexes and searches large datasets. Amazon S3 provides scalable storage for AI/ML data. This comprehensive suite enables automated operations, consistent deployments, and data-driven insights for both traditional and AI/ML components. Teams can automate deployments, monitor system health, and analyze application performance across all components.

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Amazon Cognito provides secure user authentication and authorization for the virtual assistant interface, handling complex authentication workflows and supporting enterprise identity federation. CloudFront delivers encrypted content with edge security. Amazon Bedrock helps ensure secure AI model access and execution with built-in security controls, eliminating the need for custom AI infrastructure security. AWS Identity and Access Management (IAM) implements fine-grained permissions for service-to-service communication, following a least-privilege access model. Both OpenSearch Service and DynamoDB encrypt data at rest and maintain secure access patterns for knowledge base queries and conversation history.

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CloudFront distributes content across global edge locations, helping ensure low-latency access to the web UI and preventing single point of failure for content delivery. Amazon S3 provides highly durable storage for static assets and knowledge base content with 99.999999999% durability to protect critical data. DynamoDB offers multi-AZ replication for conversation history, protecting against regional failures. Lambda functions provide distributed processing with automatic scaling and fault isolation. Amazon Bedrock supports reliable AI processing through multiple domain-specific agents. This comprehensive approach creates a highly available and fault-tolerant system, so that teams can maintain workplace productivity without interruption.

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CloudFront delivers low-latency content globally, minimizing latency for users worldwide through its global edge network. Amazon S3 offers fast access to static assets and datasets, while DynamoDB provides low-latency access to conversation history data. Lambda enables serverless processing for AI orchestration and web searches, allowing for quick scaling based on demand and cost-efficiency. Amazon Bedrock provides efficient AI processing capabilities without managing complex infrastructure. OpenSearch Service enables rapid retrieval of indexed documents. Together, these services create a highly performant and scalable approach that adapts to varying workload requirements and traffic patterns.

Read the Performance Efficiency whitepaper 

Amazon S3 offers low-cost storage with different tiers for cost-effective data management. CloudFront uses edge caching to reduce data transfer costs and improve performance. Together, these services helpminimize data transfer charges and storage costs.

Additionally, Lambda provides a serverless compute model where you only pay for consumed compute time, eliminating the need for maintaining and scaling infrastructure. DynamoDB offers an on-demand capacity mode that allows for cost-effective handling of variable workloads without overprovisioning. Amazon Bedrock enables AI processing without maintaining expensive ML infrastructure. This suite of services allows this Guidance to scale efficiently, matching resources to actual usage, while providing the performance and capabilities needed for a sophisticated employee virtual assistant.

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Lambda enables serverless computing, spinning up resources only when needed and scaling automatically based on demand. This helps ensure compute resources are used efficiently, minimizing idle time and energy waste. Amazon Bedrock provides optimized AI models, reducing the need for custom infrastructure and maximizing AI processing efficiency. DynamoDB auto-scaling capabilities adjust capacity units based on actual usage patterns, maintaining high utilization of deployed resources. CloudFront provides a global content delivery network that reduces redundant data transfers, lowering network traffic and associated energy consumption. By leveraging serverless and managed services, this Guidance minimizes the need for provisioning and maintaining physical hardware, reducing overall environmental impact.

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.