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The supplied source describes the Adaptive Agentic Intelligence Framework, an agentic AI approach built with Strands Agents. It uses local small language models through Ollama for routine tasks and Amazon Bedrock cloud…
AWS describes a pattern that lets supply chain teams ask natural-language questions in Amazon Quick and receive cross-system answers from live ERP, monitoring, procurement, and email sources. Amazon Bedrock AgentCore acts as…
The supplied source describes an AWS blog walkthrough about using AtScale as a semantic layer for agentic AI. It follows earlier customer 360 examples with Stardog and Denodo, then uses AtScale dimensional…
Smartsheet launched a generally available Model Context Protocol server on AWS, allowing AI assistants to read, write, and manage Smartsheet data through a standardized protocol. The server includes more than 40 tools…
This analysis explains how Denodo Platform provides a governed, live semantic layer over Amazon Aurora, Amazon Redshift, and on-premises PostgreSQL for an Amazon Bedrock AgentCore-hosted agent. It covers federated customer-360 views, VQL…
This educational analysis reviews an AWS walkthrough for connecting on-premises data sources to Amazon Quick privately using AWS PrivateLink. It explains that user-facing console and API traffic uses interface VPC endpoints, while…
The article describes five key challenges encountered when moving a multi‑agent system to production and explains how the Deep Insight architecture addresses each one using Strands Agents, Amazon Bedrock, AgentCore, Fargate, and…
AWS announced a generally available upgrade to the Amazon Polly Generative engine. The upgrade adds 14 new generative voices across 9 locales and makes Hindi and Finnish generative voices generally available for…
This article explains Amazon SageMaker AI's training session APIs, which allow users to run custom training loops (like SFT or RL) on managed GPU infrastructure without provisioning or managing the underlying compute.…
AWS has introduced the general availability of training sessions for Amazon SageMaker AI serverless model customization. This capability enables developers to write and coordinate custom post-training loops, including reinforcement learning and multi-turn…
The AWS AI source explains how Amazon Quick can support governed AI use for healthcare data, starting with PHI and HIPAA definitions, then mapping capabilities to safeguards.
The article explains how i14 adapted its Laplace 3B model, which uses Gated Linear Attention instead of softmax, to run on AWS Trainium2. By rewriting the attention kernel in native PyTorch, optimizing…
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