Category: Artificial Intelligence

​​​​Organizations are moving beyond single-model AI toward multi-agent architectures. In these systems, agents offload intermediate results to files rather than carrying everything in the prompt, because a large prompt inflates cost and degrades quality. A model’s context window is finite, so files become working memory that persists after a session ends. In multi-agent systems, a […]

WeatherBug is the third largest weather intelligence company in the US, delivering real-time forecasts, radar, lightning alerts, and interactive maps to over 10 million users. As their data footprint has grown across hundreds of Amazon Simple Storage Service (Amazon S3) buckets in a multi-account AWS environment, their storage costs rose steadily with no clear visibility […]

Machine learning (ML) models used for inference on Kubernetes are often several gigabytes in size. When these models are embedded in container images, images become oversized and pod scheduling slows. More critically, inference pods are inherently stateful. Model weights, tokenizer files, compiled GPU kernels, and runtime caches must persist across pod restarts, node failures, and […]

Customers managing large volumes of objects in Amazon Simple Storage Service (Amazon S3) often need to attach rich business context like compliance classifications, processing lineage, AI-generated labels, and more. Until now, this context lived in external databases or sidecar files that were stored as separate objects, which created complexity to manage and keep it up […]

The most capable multi-agent AI systems share a common trait: they give agents the right context at the right time. When agents lack access to shared history, including what other agents discovered, what tasks are already complete, and what decisions were made in previous sessions, they might duplicate work, contradict each other, and burn through […]

Organizations generate massive volumes of structured data from customer transactions, operational metrics, product catalogs, and compliance records. This data contains insights that can help businesses make better and timely decisions. Financial advisors need to review client transaction histories, retail analysts track inventory trends, and healthcare administrators monitor patient outcomes. Yet accessing these insights creates a […]

Since its launch in 2006, Amazon S3 has used a global namespace where bucket names must be unique across all AWS accounts and AWS Regions. This design has served customers well at scale, but organizations managing multiple accounts and environments often encounter naming collisions. When a bucket is deleted, its name returns to the global […]

Managing data access across multiple layers of permissions is a common industry challenge. Changes to AWS Identity and Access Management (AWS IAM) policies, Amazon Simple Storage Service (Amazon S3) bucket configurations, AWS Key Management Service (AWS KMS) key policies, or Amazon Virtual Private Cloud (Amazon VPC) endpoint policies can unintentionally cause access issues. When these […]

State-of-the-art AI agents rely on external tools to perform actions on their behalf. A tool is a function with a clear description, defined inputs, and outputs that extend the capabilities of a large language model (LLM). As toolkits expand, selecting the right tool for each task requires effective mechanisms, among which semantic search enables agents […]

As enterprises scale their adoption of generative AI, one of the biggest technical challenges is connecting AI applications to the right data and making that data fast, accessible, and secure. AI agents are transforming industries through applications like customer support automation, personalized e-commerce recommendations, and research assistance in financial services and healthcare. These applications require […]