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Observability agents are fast. They query alerts, correlate logs with traces, and produce a root cause hypothesis in minutes. The part that still takes time is verification. You read the agent’s text summary, open your observability tools in a browser, navigate to the trace waterfall, check the service map to scope impact, and cross-reference what […]

Amazon Quick Desktop brings governed, AI-assisted reporting to the files your team already manages on Amazon FSx for NetApp ONTAP (FSx for ONTAP), cutting weekly report preparation from hours to minutes. Today, producing those reports takes hours of manual effort each week. Teams re-read the same documents, reformat metrics, and copy summaries into Slack. Leaders

Learn how to use Google Search tools to find home decor inspiration, shop for furniture, and tackle DIY projects. ​ 

Today, we are announcing new Ray capabilities on Amazon SageMaker HyperPod that integrate Ray with the HyperPod purpose-built infrastructure for foundation model training and serving. Ray is an open-source framework that data scientists use to scale distributed Python workloads across clusters of GPUs, from distributed training with Ray Train to model serving with Ray Serve.

Organizations across industries struggle with managing institutional knowledge, the collective wisdom and experience accumulated over years of operations. This “tribal knowledge” often disappears when key personnel leave, creating knowledge gaps that impact efficiency and innovation. Traditional documentation methods have proven inadequate, often resulting in outdated or inaccessible information when it’s needed most. In this post,

How AWS Agent Registry and the Agentic Resource Discovery (ARD) specification enable cross-environment discovery for your agents As organizations scale their use of artificial intelligence (AI) agents and tools, finding the right resource becomes the hard part. Teams build Model Context Protocol (MCP) servers, deploy agents, and create specialized tools, but without a central catalog,

At many restaurants, a large share of orders still arrive by phone, and those calls usually land on a staff member who is already taking care of customers at the counter. Callers wait on hold, orders get written down by hand, and busy periods make both worse. Adding an app or a website helps customers

As data collection and data generation accelerate, the gap between our ability to produce raw data and our capacity to standardize it continues to widen. Without automation, this gap becomes a critical bottleneck that delays analysis, complicates interpretation, and limits the global value of shared datasets. Metadata harmonization (standardizing labels, identifiers, and formats so datasets

Data engineering teams routinely spend weeks standing up a single new data source: writing ETL, hand-writing quality checks, updating semantic models, and validating compliance. The Agentic Data Operations Platform (ADOP) on AWS is designed to significantly accelerate that timeline. It’s a reference architecture built on Amazon Bedrock and your AI coding tool of choice. Specialized

In our conversations with customers over the past months, one pattern keeps recurring. Whether they work with coding agents, autonomous agents, or human-interactive ones, and regardless of workload maturity, we start with the same question: “Which AI agents have access to customer data, who granted it, and what would exposure look like if a credential