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Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. This post is cowritten with Isaac Cameron and Alex Gnibus from Tecton.
AI agents continue to gain momentum, as businesses use the power of generative AI to reinvent customer experiences and automate complex workflows. In this post, we explore how to build an application using Amazon Bedrock inline agents, demonstrating how a single AI assistant can adapt its capabilities dynamically based on user roles.
The key to making this approach practical is to augment human agents with scalable, AI-powered virtual agents that can address callers’ needs for at least some of the incoming calls. Check out these short demo videos: Introduction to QnABot Solution Introducing Amazon Lex Try Amazon Lex or the QnABot for yourself in your own AWS account.
In this section, we briefly introduce the systemarchitecture. The following diagram illustrates this architecture. The monitoring dashboard is a lightweight demo app that provides essential features for moderators. We’ll delve deeper into live stream text and audio moderation using AWS AI services in upcoming posts.
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