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...
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...
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...
If you’re running Amazon Nova 1 models on Amazon Bedrock, you might be looking to expand your context window size, deepen reasoning capabilities, or integrate external tools for web search and code execution. Amazon Nova 2 models address these constraints while improving performance on...
This post is cowritten with Hammad Mian and Joonas Kukkonen from Bark.com.
When scaling video content creation, many companies face the challenge of maintaining quality while reducing production time. This post demonstrates how Bark.com and AWS collaborated to solve this problem, showing you a replicable...
This post is co-written with Mark Ross from Atos.
Organizations pursuing AI transformation can face a familiar challenge: how to upskill their workforce at scale in a way that changes how teams build, deploy, and use AI. Traditional AI training approaches—online courses, certification programs, and...
