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Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch
Learn how i14 trained and served Laplace 3B, a linear attention model, on AWS Trainium2 using native PyTorch. Its recurrent state stays fixed at 29.5 MB per sequence regardless of context length, avoiding the growing key-value cache of tran
AWS 人工智能 · Published 10/21/2026, 05:14Workflow context for AI agents with Smartsheet MCP on AWS
The Smartsheet Model Context Protocol (MCP) Server lets AI agents read, update, and act on live data. This post shows two ways to connect it on AWS: Amazon Quick for business teams and Kiro for developers, so your agents can work with Smart
AWS 人工智能 · Published 10/21/2026, 01:59Work management context for AI agents with Smartsheet MCP on AWS
The Smartsheet Model Context Protocol (MCP) Server lets AI agents read, update, and act on live work management data. This post shows two ways to connect it on AWS: Amazon Quick for business teams and Kiro for developers, so your agents can
AWS 人工智能 · Published 10/21/2026, 01:59From data to decisions: Conversational supply chain intelligence with Amazon Quick and Amazon Bedrock AgentCore
Enterprise supply chains are data-rich but insight-poor, with the data to decide scattered across ERP, monitoring, procurement, and email. This post shows how Amazon Bedrock AgentCore exposes those systems as a governed tool layer, and how
AWS 人工智能 · Published 10/21/2026, 01:52Fine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod
Learn how we fine-tuned GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod, and how to decide between supervised fine-tuning (SFT) and reinforcement learning with GRPO. SFT took reasoning-language accuracy from 22% to 100%,
AWS 人工智能 · Published 10/21/2026, 01:48How Octave built a secure multi-tenant AI foundation on Amazon Bedrock
Octave built a secure, multi-tenant AI foundation on Amazon Bedrock, using Cohere Embed v4 for embeddings and Amazon Bedrock AgentCore for isolated agent execution, to power a Complaint Triage Assistant that searches 2 billion regulated doc
AWS 人工智能 · Published 10/21/2026, 01:34Access OpenAI GPT models on Amazon Bedrock from Canada with cross-Region inference
Canadian organizations can now access OpenAI GPT-5.6, GPT-6, and GPT-6.1 models on Amazon Bedrock from the Canada (Central) and Canada West (Calgary) Regions using cross-Region inference. This post covers the models and inference profiles,
AWS 人工智能 · Published 10/21/2026, 00:42Discover tools at runtime with AWS Agent Registry
Hardcoded tool wiring breaks down as an agent's catalog grows from a handful of tools to hundreds. This post walks through a runtime Discover, Instantiate, Execute loop built on AWS Agent Registry, where an orchestrator discovers MCP server
AWS 人工智能 · Published 10/21/2026, 00:22Securing AI agent identity with X.509 PKI on AWS
Learn how to secure multi-agent AI systems with cryptographic identity using Keyfactor EJBCA and AWS services. This reference architecture combines signed request envelopes, user-signed delegation assertions, RFC 8693 audience-scoped tokens
AWS 人工智能 · Published 10/21/2026, 00:03How Eon built multi-cloud investigation with an MCP Gateway on Amazon Bedrock AgentCore
Learn how Eon replaced per-tool credentials and local MCP processes across AWS, GCP, and Azure with a single authenticated Amazon Bedrock AgentCore gateway, giving more than 100 developers and autonomous agents unified access to production
AWS 人工智能 · Published 10/20/2026, 23:57Build a semantic layer for agentic AI on AWS with Denodo and Amazon Bedrock AgentCore
Build a governed semantic layer for agentic AI on AWS with the Denodo Platform and Amazon Bedrock AgentCore. It federates Amazon Aurora, Amazon Redshift, and an on-premises PostgreSQL database into a single customer 360 view an agent querie
AWS 人工智能 · Published 10/20/2026, 23:39Build a semantic layer for agentic AI on AWS with Strategy Mosaic and Amazon Bedrock AgentCore
The third post in our semantic-layer series builds a customer 360 across Amazon Redshift and Amazon Aurora with Strategy Mosaic, exposes it to a Strands agent on Amazon Bedrock AgentCore through the Mosaic MCP server, and enforces per-user
AWS 人工智能 · Published 10/20/2026, 23:36Build a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCore
The third post in our semantic-layer series builds a governed customer 360 model on Amazon Redshift with AtScale, then exposes it to a Strands agent on Amazon Bedrock AgentCore through the AtScale MCP server. Identity propagation, RBAC, row
AWS 人工智能 · Published 10/20/2026, 23:33Structured 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. Receive schema-compliant JSON that conforms to a defined structure, without custom parsing, retry logic
AWS 人工智能 · Published 10/20/2026, 07:32Developers can now access SageMaker Studio through the new AWS experience
New Amazon SageMaker AI customers can now land in a fully configured SageMaker Studio environment in only a few steps. Sign up with an identity you already have, such as Amazon, Apple, GitHub, or Google, with no advanced AWS account or cred
AWS 人工智能 · Published 10/20/2026, 06:39Amazon SageMaker JumpStart: 2026 year-to-date in review
Amazon SageMaker JumpStart onboarded over 100 new models from 30 providers in the first three quarters of 2026. This recap covers the model-onboarding highlights and JumpStart enhancements, including the simplified Model Hub, pre-configured
AWS 人工智能 · Published 10/20/2026, 05:14Pegasus 1.5 from TwelveLabs is now available in Amazon Bedrock
TwelveLabs Pegasus 1.5 is now available in Amazon Bedrock. The video language model turns long-form video into structured, timestamped metadata: define the moments that matter and get every matching segment back as JSON, with new video segm
AWS 人工智能 · Published 10/20/2026, 00:49How Deepgram optimized voice AI at scale with AWS Trainium
Deepgram worked with the AWS Forward Deployed Engineering team to port its Nova-3 speech-to-text model to AWS Trainium2 for offline batch inference. This post covers porting to the AWS Neuron stack, kernel-level tuning, a data-parallel Rust
AWS 人工智能 · Published 10/20/2026, 00:23Push-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM
vLLM's prefill-decode disaggregation splits LLM inference across engine instances but must transfer the KV cache between them. This post introduces a push-based NIXL connector that moves transfer coordination off the critical path, deliveri
AWS 人工智能 · Published 10/20/2026, 00:05Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM
Multi-turn agentic workloads reprocess growing conversation context each turn, driving up latency. See how bi-directional KV cache transfer lets prefill and decode nodes reuse previously computed context across turns, cutting time to first
AWS 人工智能 · Published 10/20/2026, 00:03Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore
Autonomous AI agents fail softly: latency stays flat while answer quality quietly erodes. This post shares a nine-stage evaluation framework, built on Amazon Bedrock AgentCore, that catches silent quality regressions across the full agent l
AWS 人工智能 · Published 10/17/2026, 04:48Introducing Reflection Beam: an open-weight model for agentic coding on AWS
Reflection AI's Beam is an open-weight model for agentic coding and long-horizon tasks. This post shows how to deploy Beam on AWS: start with an Amazon SageMaker JumpStart real-time endpoint, or use Amazon SageMaker HyperPod Inference with
AWS 人工智能 · Published 10/16/2026, 23:23Meta’s Muse Spark Open now available in Amazon SageMaker JumpStart
Muse Spark Open, Meta's coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart. Deploy it to a SageMaker AI endpoint in your own AWS account using SageMaker Studio or the SageMaker Python SDK, and run your first
AWS 人工智能 · Published 10/16/2026, 01:43Trust in Depth: Giving agents more autonomy without losing control
Enterprise leaders want AI agents to take on more work but don't fully trust them to act correctly every time. Trust in Depth is AWS's layered approach-scope, know, bound, observe, and prove-that lets agents act on their own when their acti
AWS 人工智能 · Published 10/15/2026, 23:05
