Remove Architecture Remove Generative AI Remove System Design
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Agentic AI design: An architectural case study

CIO

With all this talk, you would think it is easy to define what qualifies as agentic AI, but it isn’t always straightforward. An agent is part of an AI system designed to act autonomously, making decisions and taking action without direct human intervention or interaction. Let’s start with the basics: What is an agent?

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. For instance, consider an AI-driven legal document analysis system designed for businesses of varying sizes, offering two primary subscription tiers: Basic and Pro. 70B and 8B.

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Accelerating insurance policy reviews with generative AI: Verisk’s Mozart companion

AWS Machine Learning - AI

At the forefront of using generative AI in the insurance industry, Verisks generative AI-powered solutions, like Mozart, remain rooted in ethical and responsible AI use. Data: Policy forms Mozart is designed to author policy forms like coverage and endorsements.

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How to win at AI: think like a systems designer, not a tech shopper

CIO

AI in 2025 isnt about chasing the next model its about building the muscle to deploy it responsibly, repeatedly, and at scale. IT should think like a systems designer, not a tech shopper.

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Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails

AWS Machine Learning - AI

With Amazon Bedrock and other AWS services, you can build a generative AI-based email support solution to streamline email management, enhancing overall customer satisfaction and operational efficiency. AI integration accelerates response times and increases the accuracy and relevance of communications, enhancing customer satisfaction.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

Generative AI question-answering applications are pushing the boundaries of enterprise productivity. These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques.

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Extend large language models powered by Amazon SageMaker AI using Model Context Protocol

AWS Machine Learning - AI

We will deep dive into the MCP architecture later in this post. Developed by Anthropic as an open protocol, the MCP provides a standardized way to connect AI models to virtually any data source or tool. The following diagram illustrates this workflow.