Integrating Amazon Bedrock AgentCore with Slack brings AI agents directly into your workspace. Your teams can interact with agents without jumping between applications, losing conversation history, or re-authenticating. The integration handles three technical requirements: validating Slack event requests for security, maintaining conversation context across threads, and managing responses that exceed Slack’s timeout limits. Developers typically …
This post is cowritten by Paul Burchard and Igor Halperin from Artificial Genius. The proliferation of large language models (LLMs) presents a significant paradox for highly regulated industries like financial services and healthcare. The ability of these models to process complex, unstructured information offers transformative potential for analytics, compliance, and risk management. However, their inherent …
Nemotron 3 Super is now available as a fully managed and serverless model on Amazon Bedrock, joining the Nemotron Nano models that are already available within the Amazon Bedrock environment. With NVIDIA Nemotron open models on Amazon Bedrock, you can accelerate innovation and deliver tangible business value without managing infrastructure complexities. You can power your …
Generating high-quality custom videos remains a significant challenge, because video generation models are limited to their pre-trained knowledge. This limitation affects industries such as advertising, media production, education, and gaming, where customization and control of video generation is essential. To address this, we developed a Video Retrieval Augmented Generation (VRAG) multimodal pipeline that transforms structured …
Use RAG for video generation using Amazon Bedrock and Amazon Nova Reel Read More »
A key development in generative AI is AI-powered video generation. Before AI, creating dynamic video content required extensive resources, technical expertise, and significant manual effort. Today, AI models can generate videos from simple inputs, but organizations still face challenges like unpredictable results. This post introduces Video Retrieval-Augmented Generation (V-RAG), an approach to help improve video …
Running machine learning (ML) models in production requires more than just infrastructure resilience and scaling efficiency. You need nearly continuous visibility into performance and resource utilization. When latency increases, invocations fail, or resources become constrained, you need immediate insight to diagnose and resolve issues before they impact your customers. Until now, Amazon SageMaker AI provided …
Organizations with users in multiple geographies face data residency requirements such as General Data Protection Regulation (GDPR) in Europe, country-specific data sovereignty laws, and internal compliance policies. Amazon Quick with Microsoft 365 extensions supports Regional routing to meet these requirements. Amazon Quick supports multi-Region deployments so you can route users to AWS Region-specific Amazon Quick …
Enforce data residency with Amazon Quick extensions for Microsoft Teams Read More »
With a wide array of Nova customization offerings, the journey to customization and transitioning between platforms has traditionally been intricate, necessitating technical expertise, infrastructure setup, and considerable time investment. This disconnect between potential and practical applications is precisely what we aimed to address. Nova Forge SDK makes large language model (LLM) customization accessible, empowering teams …
Kick off Nova customization experiments using Nova Forge SDK Read More »
Large language models (LLMs) have transformed how we interact with AI, but one size doesn’t fit at all. Out-of-the-box LLMs are trained with broad, general knowledge and improved for a wide range of use cases, but they often fall short when it comes to domain-specific tasks, proprietary workflows, or unique business requirements. Enterprise customers increasingly …
Introducing Nova Forge SDK, a seamless way to customize Nova models for enterprise AI Read More »
Moving AI agents from prototypes to production surfaces a challenge that traditional testing is unable to address. Agents are flexible, adaptive, and context-aware by design, but the same qualities that make them powerful also make them difficult to evaluate systematically. Traditional software testing relies on deterministic outputs: same input, same expected output, every time. AI …
Evaluating AI agents for production: A practical guide to Strands Evals Read More »
