AI

Training a multi-turn agent in Amazon SageMaker AI to resolve support tickets or moderate content means handling a sequence of dependent steps, not a single response. These agents read instructions, make tool calls, read the results, decide the next action, and recover from a mistake before committing to an answer. That flexibility is also what […]

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Without federal support for curiosity-driven research, the innovation and talent pipeline that has helped ensure our nation’s prosperity and safety could run dry, warned President Sally Kornbluth during a Washington Post Live event.  During “The Next Generation,” a panel discussion moderated by Washington Post reporter Zachary Goldfarb at The Washington Post’s “Building America Summit,” Kornbluth

Touted as a less-hookup-focused Grindr, Goose is an invite-only space for gay men. The problem is the people promoting it don’t seem real.

AWS customers can once again access Mythos-level capabilities and exceptional performance in coding, knowledge work, and vision.

Government agencies running workloads in AWS GovCloud (US) need AI capabilities that keep pace with the commercial sector. At the same time, they can’t compromise the security and compliance controls their missions require. As open-weight foundation models (FMs) move from experimentation into mission systems, two requirements shape every model decision. First, the model must deliver

Are you worried your AI chatbot is trying to build a bomb or leak personal information about you? There’s a website for that.

As enterprises deploy AI agents across teams, vendors, and infrastructure, managing agent-to-agent communication becomes a growing operational burden. Without a centralized layer, each new agent integration adds point-to-point connections, separate credentials, and custom routing logic. Teams spend engineering cycles wiring up connectivity instead of building agent capabilities. Access control becomes fragmented, with no single place

Let’s say your customer support agent asks for “billing issues”, and gets back technical support tickets, sales conversations with receipt issues, and billing disputes all mixed. This is the retrieval precision wall that teams hit once their agents accumulate weeks of interaction history: similarity search finds everything that’s semantically close for this customer but does

Large language models (LLMs) have transformed how we process and generate information, but they still struggle with effectively integrating knowledge across multiple sources. Standard Retrieval Augmented Generation (RAG) methods, although helpful, often fall short when tackling multi-hop reasoning tasks that require connecting information from separate documents. To address these limitations, we explore HippoRAG, a novel