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Favorite AI agents increasingly handle work that depends on what changed today, such as security advisories, product releases, prices, and regulations. A model’s training data can’t answer those questions, so agents need web search. For production use, teams also need results they can cite, and control over which sources count.
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Shared by AWS Machine Learning November 11, 2026
Favorite Kimi K3.1 Pro Highspeed from Moonshot AI is now available on Amazon Bedrock. Part of Moonshot AI’s new K3.1 series, it’s a post-trained version of Kimi K3 with the same 3T-class open-weight architecture and 1-million-token context window, trained with substantially more post-training compute. According to Moonshot AI, that extra
Read More Shared by AWS Machine Learning November 11, 2026
Favorite Usage-based metering for bidirectional streaming solves a software billing problem for real-time speech AI. Customers deploying streaming models on Amazon SageMaker AI bidirectional streaming pay for the endpoint infrastructure that hosts the model. Separately, model providers listing on AWS Marketplace need a way to charge for what customers consume
Read More Shared by AWS Machine Learning November 11, 2026
Favorite Teams building AI agents on AWS use a range of frameworks, including LangGraph, LangChain, Strands Agents, and Amazon Bedrock AgentCore. Each framework records agent execution in its own way, so the structure of messages, tool calls, and handoffs between agents differs from one to the next. Evaluation tooling tends
Read More Shared by AWS Machine Learning November 11, 2026
Favorite This blog post focuses on how AI can improve and expedite the product development life cycle using agents built on AWS Quick, with human approval baked into the process. AI-driven software delivery is helping teams ship products faster than ever. As implementation accelerates, the upstream product development process becomes
Read More Shared by AWS Machine Learning November 11, 2026
Favorite If you’re building generative AI solutions, reusable assets can help you stop rebuilding components that already exist: Retrieval Augmented Generation (RAG) pipelines, document processors, agent scaffolding, and so on. This has a compounding cost: time not spent solving the customer’s actual problem. This post shows you how to access
Read More Shared by AWS Machine Learning November 11, 2026
Favorite This post is co-written with Nick Tamburro of DXC Technology. When an AI agent can act on a production server at a bank or a hospital, access to a capable foundation model is only the starting point. Recent public incidents, in which autonomous agents took destructive actions their operators
Read More Shared by AWS Machine Learning November 11, 2026
Favorite Every time a Model Context Protocol (MCP) server published in the AWS Agent Registry, a capability of Amazon Bedrock AgentCore, gains a new tool or updates its schema, the registry record must be updated too. Without automated push-based sync, consumers discover stale metadata. For organizations managing multiple MCP servers
Read More Shared by AWS Machine Learning November 11, 2026
Favorite From November 30th to December 4th, Amazon Web Services (AWS) will hold its annual premier learning event: re:Invent. At this event, attendees will become stronger and more proficient in various areas of AWS technology through a variety of experiences: keynotes given by AWS leaders, interactive working sessions led by
Read More Shared by AWS Machine Learning November 11, 2026
Favorite At AWS, we believe AI builders should be able to choose the right model for every task, trust their agents in production, and see clearly what their AI workloads cost. That belief guides how we invest across our portfolio. Amazon Bedrock gives you access to leading models and the
Read More Shared by AWS Machine Learning November 11, 2026