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Optimizing AI Agent Experiences: Leading Providers, Gaps, and Human Support Strategies

eglobalis

Optimizing AI Agent Experiences: Leading Providers, Gaps, and Human Support Strategies Introduction Artificial intelligence agents are rapidly transforming customer service and enterprise operations. In practice, the most effective customer experiences blend cutting-edge AI with timely human support.

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Minimize generative AI hallucinations with Amazon Bedrock Automated Reasoning checks

AWS Machine Learning

Through logic-based algorithms and mathematical validation, Automated Reasoning checks validate LLM outputs against domain knowledge encoded in the Automated Reasoning policy to help prevent factual inaccuracies. This makes sure that business rules and policies are accurately captured and maintained by those who understand them best.

Policies 128
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How to Lead a B2B CX Transformation Program—And Avoid Costly Mistakes

ECXO

CX transformation often requires breaking entrenched habits and coordinating across silos, which wont happen without active support from the C-suite. While customer delight is the ultimate goal, framing it in terms of ROI and competitive advantage speaks the language of executives and ensures CX strategy gets the necessary support.

B2B 339
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Using responsible AI principles with Amazon Bedrock Batch Inference

AWS Machine Learning

Have an AWS Identity and Access Management (IAM) role for batch inference with a trust policy and Amazon S3 access (read access to the folder containing input data and write access to the folder storing output data). Refer to Supported Regions and models for batch inference for a complete list of supported models.

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Growth vs. Customer Experience: A Dilemma?

ECXO

Map the Customer Journey What to Do: Identify every touchpoint a customer has with your business, from awareness to post-purchase support. Example Action: Synchronize your customer support systems to provide unified responses across email, chat, and phone. Highlight pain points, friction areas, and moments of delight.

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Dynamic video content moderation and policy evaluation using AWS generative AI services

AWS Machine Learning

Organizations across media and entertainment, advertising, social media, education, and other sectors require efficient solutions to extract information from videos and apply flexible evaluations based on their policies. You can use the solution to evaluate videos against content compliance policies.

Policies 134
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Dynamic metadata filtering for Amazon Bedrock Knowledge Bases with LangChain

AWS Machine Learning

This approach can be applied to other use cases, such as customer support, personalized recommendations, and content curation, where context-sensitive information retrieval is essential. For Amazon Bedrock: Use IAM roles and policies to control access to Bedrock resources and APIs.