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A recap of the biggest Amazon Bedrock, AgentCore, and Strands updates from August 2026. At AWS, we have long focused on making foundational technologies accessible and providing the infrastructure needed to put them to work. Amazon Bedrock, used by more than 225,000 active customers, including over 80% of Fortune 100 companies, embodies that commitment by […]

Heurist uses Amazon Bedrock AgentCore to build AI-powered financial intelligence for retail investors. Its flagship product, Heurist Finance, brings several institutional-style workflows into one chat experience: it gathers market data, reads filings and news, runs deep research, builds and stress-tests portfolios, and monitors positions. Each answer reflects the user’s portfolio and preferences. Heurist’s goal is

Track live game feeds, explore detailed stats, and get custom fantasy recommendations directly in Search this season. ​ 

Discover how filmmakers and Google DeepMind used AI to recreate a couple’s unrecorded past in the short film “Love, Rendered.” ​ 

TorchServe is no longer actively maintained. The official project notice states there are no planned updates, bug fixes, new features, or security patches, and that vulnerabilities might not be addressed. For teams that run model inference on TorchServe today, this means security patches stop and compatibility updates with newer versions of PyTorch and CUDA stop.

As Amazon Quick environments scale and new AI-powered capabilities expand what users can do, automating user-level custom permissions becomes critical to maintaining the principle of least privilege. To address this, with custom permissions in Quick, you can enforce fine-grained access control by toggling specific features on or off for individual users. For example, with custom

GPT-6 Astra from OpenAI brings greater depth and judgment to your most demanding tasks and runs on the Amazon Bedrock inference engine built for high performance, security, and scale. Organizations are already running AI agents that write code, analyze data, and automate complex workflows at production scale on Amazon Bedrock. GPT-6 Astra raises the potential

As AI systems take on more complex tasks, much of the industry’s progress has come from increasing model scale, training data, context length, and inference-time computation. Instead of externalizing reasoning work as a chain-of-thought (generating extra tokens sequentially and feeding them back into later steps), Pathway’s brain-inspired BDH (Dragon Hatchling) performs reasoning in latent space.

We are excited to announce feature-level writes for Amazon SageMaker Feature Store. Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage machine learning (ML) features, the processed data used for training models and generating predictions. With the new UpdateRecord API, you can now update one or more feature values

Governing models across accounts is the natural next step once automatic model registration is in place. In Part 1 we introduced how managed MLflow on Amazon SageMaker AI synchronizes registered models into the SageMaker AI Model Registry. We walked through a single-account setup where AWS Identity and Access Management (IAM) condition keys separate the data