AWS introduced a Consent portal for Amazon Bedrock AgentCore Identity, providing a managed web interface and a session binding endpoint for AgentCore Gateway. The setup lets developers provision a portal, configure GitHub and Slack three-legged OAuth targets, and run an end-user consent flow. Activity can then be reviewed through AWS logging tools.
AWS published an eight-step decision framework to help teams choose how much to customize generative AI models. The guidance spans prompt engineering, retrieval-augmented generation, fine-tuning, continued pre-training, and Amazon Nova Forge, recommending that teams start with simpler methods and escalate only when needed.
An AWS Machine Learning Blog tutorial describes how to combine a demand-forecasting model (MMF) with Databricks Genie and Amazon Quick to automate retail replenishment. The pipeline detects demand spikes, checks them against current supplier stock, and then places orders without human intervention. The post frames forecasting as largely solved and argues the real difficulty lies in acting on those predictions.
Amazon published a walkthrough for running the open-weight Qwen3.8-2.4T-A95B model, which has 2.4 trillion parameters, on its SageMaker HyperPod service using the vLLM inference engine. The guide covers setting up the cluster, applying NVFP4 quantization, and exposing an OpenAI-compatible endpoint. It also notes support for tool calling, reasoning, and multi-token prediction speculative decoding.
Amazon published a roundup of AI developer features it shipped in August 2026, spanning Amazon Bedrock, Bedrock AgentCore and the Strands framework. Highlights include million-token context windows for OpenAI models, cross-Region inference, and agent workloads that can run for as long as 14 days on dedicated compute. The post also notes wider AWS GovCloud availability and a Strands offering aimed at robotics.
With TorchServe no longer maintained, teams must manage their own GPU inference stack. AWS's Ray Serve Deep Learning Container bundles the framework, GPU drivers, and serving layer in a pre-tested package. The post demonstrates deploying a vision-language model using this container.
AWS will use Qualcomm hardware for AI inference workloads, according to reports. Qualcomm is developing custom chips for AWS across multiple product generations, focusing on inference tasks. Qualcomm is also relying on AWS Bedrock to help design those chips.
Qualcomm is designing custom silicon for AWS spanning several product generations, with an emphasis on AI inference workloads. The arrangement expands Amazon's use of purpose-built chips alongside its existing in-house processors for cloud AI services.