Here are Google’s latest AI updates from June 2026.

AWS GovCloud (US) now supports OpenAI's open-weight GPT OSS models (120B and 20B) and NVIDIA Nemotron models (Nano 9B v2, Nano 12B v2, Nano 30B, Super 120B) via Amazon Bedrock. Inference runs entirely within the US on infrastructure operated by US citizens, meeting FedRAMP, DoD SRG, and other compliance frameworks.

A comprehensive guide to building a serverless A2A gateway on AWS that centralizes agent management, supports path-based routing, fine-grained access control, and semantic search, enabling standardized communication without client modifications.

Learn how metadata filtering in Amazon Bedrock AgentCore Memory enhances retrieval precision. By adding attribute-based filters on top of namespace isolation, agents can scope searches by business dimensions like priority, department, or time range. The article details the three-phase lifecycle of metadata (configuration, ingestion, retrieval), highlights strictly-consistent extraction, and provides best practices for multi-agent and multi-tenant architectures.

In this post, we demonstrate how to implement HippoRAG using a comprehensive AWS stack. We use Amazon Bedrock for LLM capabilities, Amazon Neptune for graph database functionality, Amazon Neptune Analytics for advanced graph algorithms including Personalized PageRank, and Amazon Titan Embeddings for vector representations. This implementation showcases how to build and deploy HippoRAG within AWS infrastructure for enterprise-scale applications.

OpenWiki generates and maintains codebase documentation so coding agents can find the repo context they need without loading everything into one instruction file.

Inscribe developed an agentic AI system using Amazon Bedrock that reasons across documents like an expert fraud analyst. The system detects tampered, fabricated, and AI-generated financial documents in under 90 seconds, achieving a 20x improvement over manual review while maintaining accuracy and explainability for financial regulations.

This post demonstrates how to deploy BoltzGen on SageMaker AI and run an end-to-end protein design experiment. The setup offers two execution modes for different stages of research and uses step-level caching to reduce compute expenses during iterative workflows.