Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration

We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation. We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation. Our approach addresses traditional software testing limitations by leveraging LLMs such as Gemini and Mistral, multi-agent …

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Transforming enterprise operations: Four high-impact use cases with Amazon Nova

Since the launch of Amazon Nova at AWS re:Invent 2024, we have seen adoption trends across industries, with notable gains in operational efficiency, compliance, and customer satisfaction. With its capabilities in secure, multimodal AI and domain customization, Nova is enhancing workflows and enabling cost efficiencies across core use cases. In this post, we share four …

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Build a device management agent with Amazon Bedrock AgentCore

The proliferation of Internet of Things (IoT) devices has transformed how we interact with our environments, from homes to industrial settings. However, as the number of connected devices grows, so does the complexity of managing them. Traditional device management interfaces often require navigating through multiple applications, each with its own UI and learning curve. This …

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How AI can scale customer experience — online and IRL

Customer service teams at fast-growing companies face a challenging reality: customer inquiries are growing exponentially, but scaling human teams at the same pace isn’t always sustainable.  Intelligent AI tools offer a new path forward. They handle routine questions automatically so employees can focus on more complex customer service tasks that require empathy, judgment, and creative …

FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion Language Models

Autoregressive language models (ARMs) deliver strong likelihoods, but are inherently serial: they generate one token per forward pass, which limits throughput and inflates latency for long sequences. Diffusion Language Models (DLMs) parallelize across positions and thus appear promising for language generation, yet standard discrete diffusion typically needs hundreds to thousands of model evaluations to reach …

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Transforming the physical world with AI: the next frontier in intelligent automation 

The convergence of artificial intelligence with physical systems marks a pivotal moment in technological evolution. Physical AI, where algorithms transcend digital boundaries to perceive, understand, and manipulate the tangible world, will fundamentally transform how enterprises operate across industries. These intelligent systems bridge the gap between digital intelligence and physical reality, unlocking unprecedented opportunities for efficiency …

Agile AI architectures: A fungible data center for the intelligent era

It’s not hyperbole to say that AI is transforming all aspects of our lives: human health, software engineering, education, productivity, creativity, entertainment… Consider just a few of the developments from Google this past year: Magic Cue on the Pixel 10 for more personal, proactive, and contextually-relevant assistance; our viral Nano Banana Gemini 2.5 Flash image …

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Over Palantir

Antwoorden op veelgestelde vragen over Palantir Noot van de redactie: Dit bericht beantwoordt veelgestelde vragen over Palantir. Een eerdere versie werd gepubliceerd in 2022. We krijgen veel uiteenlopende vragen over Palantir. Omdat er recent ook veel interesse is getoond in ons bedrijf, hebben we de meest gestelde vragen verzameld en beantwoord in deze blog. Lees hier hoe …

Local Mechanisms of Compositional Generalization in Conditional Diffusion

Conditional diffusion models appear capable of compositional generalization, i.e., generating convincing samples for out-of-distribution combinations of conditioners, but the mechanisms underlying this ability remain unclear. To make this concrete, we study length generalization, the ability to generate images with more objects than seen during training. In a controlled CLEVR setting (Johnson et al., 2017), we …