Category: Amazon Bedrock

Organizations that receive files from external partners through SFTP face a persistent operational challenge: routing each file to the correct downstream system. Invoices, contracts, images, CSVs, and reports all arrive in a single landing zone, and each requires a different destination. The traditional approach—pattern-matching on file names with regular expressions—is inherently fragile. It relies on […]

Semiconductor verification teams run thousands of simulation jobs every night using Electronic Design Automation (EDA) tools. A large verification environment can generate logs from 100,000 or more test executions per night. A single regression cycle produces simulation logs, compilation logs, and scheduler logs. For a regression with dozens of failures, manual triage typically takes 45–60 […]

​​​​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 […]

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 […]

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 […]

Today, consumers navigate thousands of products on e-commerce sites, hundreds of shows on streaming platforms, and countless options in digital marketplaces. This choice overload creates decision fatigue, yet consumers continue to demand more variety and make more purchases online. As a result, personalization has become essential—consumers reward brands that deliver relevant, tailored online experiences. However, […]

Retrieval-Augmented Generation (RAG) is a technique that optimizes large language model (LLM) outputs by referencing authoritative knowledge bases outside of the model’s training data before generating responses. This addresses common limitations of traditional LLMs, such as outdated knowledge, hallucinated facts, and misinterpreted terminology. Organizations can implement RAG to enhance their generative AI applications with current, […]