Architecture 2

Build self-improving document processing using Amazon Bedrock AgentCore

IDC research shows that document-related challenges cost organizations $19,732 for each information worker annually and reduce productivity by 21.3 percent. Organizations receiving invoices from hundreds of suppliers often face layouts that vary by issuer and change without notice. Template-based systems require constant maintenance at this scale. Staff can spend up to 30 percent of their …

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Predictable AI spend in BigQuery: Introducing cost management for generative AI functions

The generative AI functions in BigQuery (AI.GENERATE, AI.IF, AI.CLASSIFY, AI.SCORE, and more) bring large language models (LLMs) directly into your SQL, so a single query can classify millions of support tickets or score each product review for sentiment. However, because these functions are billed by the token, scaling them across massive datasets highlights the importance …

Mix, Don’t Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings

For most languages of the world, language model pre-training operates in a data-constrained regime where models must repeat their training data many times, degrading generalization. Two remedies exist: aggressive hyperparameter tuning such as high weight decay, and mixing in data from a high-resource auxiliary language to directly aid the low-resource target. While hyperparameter tuning regularizes …

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Building agents that transact with Anchor Browser and AgentCore payments

AI agents increasingly need to access web resources that are priced per request. For example, premium datasets charge by the query, research APIs meter by the call, and paywalled publishers gate content behind payment. When an AI agent encounters one of these resources, the server returns HTTP 402 Payment Required. The agent has no built-in …

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Gemini 4 Argon available on Google Cloud, built to help solve your business’ hardest problems

Last week we announced that Gemini Enterprise has evolved to make it even simpler to get work done, and introduced the Gemini agent, your new universal agent for work. It’s always on and ready to answer your questions. It will write code, complete longer and more complex tasks, or perform whatever you need to get …

Large Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged

Large Language Models (LLMs) are considered to be a more efficient and cost-effective alternative to human judgment for Machine Translation (MT) evaluation. With MT evaluation spanning a large number of language pairs, domains and levels of annotation granularity, LLMs must be thoroughly evaluated across these dimensions before being reliably used as alternatives to human evaluation. …

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Accessing OpenAI GPT models on Amazon Bedrock with global cross-Region inference in Thailand, Malaysia, Singapore, Indonesia, and Taiwan

Single-Region inference can throttle when demand exceeds local capacity. Teams in Thailand, Malaysia, Singapore, Indonesia, and Taiwan need access to the newest OpenAI models beyond that capacity. Amazon Bedrock offers OpenAI GPT-6 Sol and GPT-6 Luna through global cross-Region inference from all five AWS Regions, and GPT-6 Astra from Asia Pacific (Singapore). This post shows …

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ICYMI: What landed for AI builders in September 2026

A recap of the latest Amazon Bedrock, Amazon Bedrock AgentCore, and Strands updates from September 2026 At AWS, we believe the fastest path to enterprise AI is giving builders real choice at every layer: model, runtime, and tooling. That belief shapes how we invest across Amazon Bedrock, AgentCore, and Strands. Amazon Bedrock continues expanding access …

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Welcome to Gemini at Work 2026: Introducing the Gemini agent

Editor’s note: This article is adapted from Thomas Kurian’s keynote address at Gemini at Work 2026. It includes a number of announcements: Gemini, a single, universal agent for work that answers your questions, handles your knowledge work, creates your images and media, and writes and runs code.  Inline in Workspace, Gemini works directly inside Gmail, …

Normalizing Trajectory Models

Diffusion-based models decompose sampling into many small Gaussian denoising steps, an assumption that breaks down when generation is compressed to a few coarse transitions. Existing few-step methods address this through distillation, consistency training, or adversarial objectives, but sacrifice the likelihood framework in the process. We introduce Normalizing Trajectory Models (NTM), which models each reverse step …