11. Build a semantic layer for agentic AI on AWS with Strategy Mosaic and Amazon Bedrock AgentCore This post is co-written with Zhili Cheng, Principal Product Manager, George Earle, Director, AI Engineering, and Kyler K, Sales Engineer of Strategy. In the first post in this seri 10/20 23:36 ♡
12. Build a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCore In the first post in this series , we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift . We ran a Strands agent on Amazon Bedroc 10/20 23:33 ♡
13. Structured outputs for Anthropic Claude models on Amazon Bedrock Amazon Bedrock now supports structured outputs for all Anthropic Claude models, on both the bedrock-runtime and bedrock-mantle endpoints, where the model is available. Whether you 10/20 07:32 ♡
14. Developers can now access SageMaker Studio through the new AWS experience Today, we’re excited to announce that new Amazon SageMaker AI customers can now land inside a fully configured Amazon SageMaker Studio environment in only a few steps after signing 10/20 06:39 ♡
15. Amazon SageMaker JumpStart: 2026 year-to-date in review Amazon SageMaker JumpStart is a machine learning (ML) hub within Amazon SageMaker AI that provides access to a curated catalog of AI models, solution templates, and example noteboo 10/20 05:14 ♡
16. Pegasus 1.5 from TwelveLabs is now available in Amazon Bedrock On Oct 9, 2026 we announced the availability of TwelveLabs’ Pegasus 1.5 on Amazon Bedrock . Pegasus 1.5 is a video language model that turns long-form video into structured, timest 10/20 00:49 ♡
17. How Deepgram optimized voice AI at scale with AWS Trainium As voice becomes a first-class modality for AI, Deepgram’s models span speech-to-text, text-to-speech, and voice agents. They now process quadrillions of audio tokens a year for mo 10/20 00:23 ♡
18. Push-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM vLLM is an open-source inference and serving engine for large language models (LLMs). LLM inference has two distinct phases: prefill, which processes the input prompt and builds th 10/20 00:05 ♡
19. Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM Multi-turn agentic workloads build on accumulated context as an agent reasons, invokes tools, and incorporates earlier results. With each new turn, the client resends the conversat 10/20 00:03 ♡
20. Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore The North Star Every organization racing to deploy AI agents faces the same reckoning: an agent that demos beautifully can quietly degrade in production, and by the time a human no 10/17 04:48 ♡
