Managing costs across multiple AWS accounts often requires finance teams to query data from several sources to get a complete view of spending and optimization opportunities. In this post, you learn how to build a FinOps agent using Amazon Bedrock AgentCore that helps your finance team manage AWS costs across multiple accounts. This conversational agent consolidates …
Build a FinOps agent using Amazon Bedrock AgentCore Read More »
Compliance audits require comprehensive evidence trails, often involving hundreds of screenshots across multiple systems. Your compliance teams likely spend hours manually navigating through GitHub repositories, AWS consoles, and internal applications, capturing screenshots at each step. This manual process is time-consuming, error-prone, and difficult to reproduce consistently across audit cycles. This post demonstrates how we automated …
Building an AI powered system for compliance evidence collection Read More »
Quality assurance (QA) automation is critical for modern software delivery. It catches regressions before production, validates user journeys at scale, and enables confident feature releases. But traditional QA automation solutions are brittle and demand specialized programming knowledge, decelerating software delivery. Automation frameworks rely on implementation details including UI selectors, element identifiers, and structural references to …
Accelerating software delivery with agentic QA automation using Amazon Nova Act Read More »
I’m excited to announce that AWS Security Agent on-demand penetration testing and AWS DevOps Agent are now generally available, representing a new class of AI capabilities we announced at re:Invent called frontier agents. These autonomous systems work independently to achieve goals, scale massively to tackle concurrent tasks, and run persistently for hours or days without …
AWS launches frontier agents for security testing and cloud operations Read More »
Veo 3.1 Lite is now available in paid preview through the Gemini API and for testing in Google AI Studio.
DevOps used to be predictable: same input, same output, binary success, static dependencies, concrete metrics. You could control what you could predict, measure what was concrete, and secure what followed known patterns. Then agentic AI arrived, and everything changed. Agents operate non-deterministically; they don’t follow fixed patterns. Ask the same question twice, get different answers. …
This post is cowritten with David Kim, and Premjit Singh from Ring. Scaling self-service support globally presents challenges beyond translation. In this post, we show you how Ring, Amazon’s home security subsidiary, built a production-ready, multi-locale Retrieval-Augmented Generation (RAG)-based support chatbot using Amazon Bedrock Knowledge Bases. By eliminating per-Region infrastructure deployments, Ring reduced the cost …
How Ring scales global customer support with Amazon Bedrock Knowledge Bases Read More »
This post is cowritten by Sebastian Angersbach, Philip Trempler, and Weiran Zhang from Volkswagen Group. Volkswagen Group stands as one of the world’s largest automotive manufacturers, delivering 6.6 million vehicles in the first nine months of 2025. The Group comprises ten distinct brands from five European countries: Volkswagen, Volkswagen Commercial Vehicles, ŠKODA, SEAT, CUPRA, Audi, …
Reimagine marketing at Volkswagen Group with generative AI Read More »
The effective monitoring and characterization of solar flares demands sophisticated analysis of X-ray emissions across multiple energy spectrums. Machine learning-based anomaly detection serves as a powerful tool for identifying significant patterns that could indicate notable solar activity. Through the identification of distinct radiation signatures, key solar event characteristics can be detected, analyzed, and comprehensively understood. …
Build a solar flare detection system on SageMaker AI LSTM networks and ESA STIX data Read More »
Deliver hyperpersonalized viewer experiences with an agentic AI movie assistant using Amazon Nova Sonic 2.0Recommendation systems are the backbone of modern media streaming services, shaping how users discover content. Traditional machine learning (ML) systems use collaborative or content-based filtering to predict content preferences. However, they often miss context-dependent needs, such as time of the day, …
