AWS

Amazon Bedrock AgentCore Payments, built with Coinbase and Stripe, is now generally available after a May preview. The service lets AI agents autonomously pay for paywalled content, APIs, and per-inference compute using stablecoin wallets with spending caps and audit logging.

NVIDIA's Nemotron 3.5 Lightning, a 30B-parameter hybrid Mixture-of-Experts model with only 3B active parameters, is now available for one-click deployment on Amazon SageMaker JumpStart. NVIDIA claims up to 4x higher throughput and 30% faster task completion for high-volume agentic workloads compared to larger frontier models.

AWS published a technical guide on designing custom composite reward functions for multi-turn reinforcement fine-tuning (RFT) of Amazon Nova models via Nova Forge's Bring Your Own Orchestration (BYOO) capability. The post covers GRPO-based reward scoring, combining outcome rewards, behavioral rewards, and penalties, plus a serverless multi-turn RL option now generally available.

Amazon has released Microsoft 365 extensions for its Quick AI assistant, embedding agentic capabilities directly into Word, Excel, PowerPoint, and Outlook. The extensions run entirely in the cloud, require no client-side installation, and connect to existing Quick data sources like Salesforce, Jira, Slack, and SharePoint.

AWS and OpenAI have made Daybreak Red (GPT-5.6 Cyber) and Daybreak Blue (GPT-5.6 Sol) available to eligible customers on Amazon Bedrock in US East (N. Virginia). The purpose-trained cybersecurity models run under AWS's zero-operator-access infrastructure and require enrollment in OpenAI's Trusted Access for Cyber program.

Branded communications company First Orion adopted Amazon Nova Act as a pre-release partner in March 2025 to replace fragile Selenium and Playwright test scripts with natural-language QA automation. The shift let QA analysts author tests directly without waiting on automation engineers to translate test cases into code.

AWS published a production reference architecture for deploying Anthropic's Claude apps gateway, a self-hosted governance layer that sits between Claude Code, Claude Desktop, and Amazon Bedrock or Claude Platform on AWS. The deployment pattern centralizes SSO authentication, model access policy, and spend controls for enterprise rollouts.

Amazon Bedrock AgentCore now supports temporal policies—stateful authorization rules that evaluate an AI agent's entire session history, not just individual tool calls. The feature runs at the AgentCore Gateway, outside agent code, so it cannot be bypassed by prompt manipulation or agent bugs.

Amazon Bedrock AgentCore now supports temporal policies that evaluate sequences of agent actions, not just single calls, powered by a new open-source policy language called Dogwood. AWS also added rate limiting at the gateway layer to cap token, request, and connection consumption per user.

AWS published a technical guide detailing two methods for keeping Claude Code inference confined to a single AWS Region: Anthropic's newer Mantle endpoint and the classic Bedrock Invoke API with application inference profiles. The right path depends entirely on which Region compliance teams require.

AWS published a suite of six Agent Skills that automate the full lifecycle of Amazon Bedrock Automated Reasoning policies—from rule extraction to deployment—directly from coding agents like Claude Code, Cursor, Kiro, and Codex. The skills wrap Bedrock's formal-logic verification APIs in structured workflows built on Anthropic's open Agent Skills format.

AWS published a technical breakdown of an MCP bridge that connects cloud-hosted AI agents on Amazon Bedrock AgentCore to MCP servers running on a user's local machine. The architecture, used internally in a finance assistant with over 41,000 conversations in its first year, tunnels MCP JSON-RPC messages through WebSocket and browser native messaging.

Amazon has launched Web Search on Amazon Bedrock, a built-in server-side tool that grounds foundation model responses in current web knowledge with zero data egress by default. The feature is enabled via a single parameter in the OpenAI Responses API and launches with OpenAI models on Bedrock's next-generation inference engine.

Amazon has added automated policy refinement to Automated Reasoning checks in Bedrock Guardrails, replacing manual hand-editing of formal logic with two new refinement modes. Every proposed change still requires human approval before it takes effect.

Moonshot AI released Kimi K3 on July 27, 2026, a 2.8 trillion parameter Mixture-of-Experts model with a 1 million token context window and native multimodal support. AWS has published a deployment guide covering SageMaker HyperPod and Amazon EKS using ml.p6-b300.48xlarge instances with 8 NVIDIA B300 Blackwell Ultra GPUs.

OpenAI's GPT-5.6 Sol, Terra, and Luna models are now generally available on Amazon Bedrock, accessible through the OpenAI-compatible Responses API. The release introduces explicit prompt caching, letting developers manually mark cache boundaries for a 90% discount on reused input tokens.

Amazon is reportedly scaling back active development of its Nova Premier, Nova Omni, Reel, and Canvas AI models, according to Business Insider. Resources are shifting to a new Frontier Model Research group led by Pieter Abbeel, with a new foundation model reportedly planned for re:Invent this fall.

Guardoc Health built a multi-stage document processing pipeline on Amazon Nova Pro, Nova Lite, and Titan Text Embeddings to extract and classify medical conditions from clinical PDFs at scale. The company claims a 46 percent reduction in documentation errors, 70 percent fewer audit fines, and over $400K in annual ROI for a single facility.

Anthropic's Claude Opus 5 is now available on Amazon Bedrock and Claude Platform on AWS, marking the first release in the company's fifth-generation Opus line. Anthropic claims the model matches its top-tier Claude Fable 5 intelligence at Opus-tier pricing, though specific pricing and benchmark figures remain undisclosed.

OpenAI's GPT-5.6 family — Sol, Terra, and Luna — is now generally available on Amazon Bedrock through a new bedrock-mantle endpoint. All three models share a 272K-token context window, support image input, and let developers dial reasoning effort from none to max.

Amazon Bedrock Managed Knowledge Bases now offers agentic retrieval through a new AgenticRetrieveStream API that decomposes multi-part questions into sub-queries and iterates until it judges evidence sufficient. The managed model costs $4 per 1,000 agentic retrieval calls plus $1 per 1,000 underlying Retrieve API calls.

Amazon Web Services launched Managed Knowledge Base for Bedrock in general availability, offering a fully managed retrieval solution with six native enterprise connectors including SharePoint, Confluence, and Google Drive. The service handles document parsing up to 500 MB for PDFs, 2 GB for audio, and 10 GB for video, with real-time access control list verification at query time.

xAI has made Grok 4.3 generally available on Amazon Bedrock, marking xAI's debut as a Bedrock model provider. The multimodal model offers a 1 million token context window, configurable reasoning effort (none/low/medium/high), and runs on Bedrock's Mantle inference engine using OpenAI-compatible APIs.

AWS has released AgentCore, a new platform for hosting and running voice-based AI agents, integrated with Amazon Nova 2 Sonic for real-time speech capabilities. The platform uses the open Model Context Protocol (MCP) to connect agents to backend systems and deploys each conversation in isolated microVMs.

AWS has extended its QA Studio reference solution with test suite functionality and a command-line interface for CI/CD integration. The updates enable parallel execution of regression tests on Amazon ECS Fargate and bring Amazon Nova Act-powered visual testing into automated deployment pipelines.

AWS has released a cloud-deployed UX testing platform built on Amazon Nova Act, a multimodal foundation model that navigates web interfaces through visual understanding rather than hard-coded selectors. The solution processes documentation with Claude 4.5 Sonnet to generate test scenarios, executes parallel testing via ECS, and analyzes results automatically, addressing the scalability limitations of manual testing and maintenance overhead of traditional automation tools.

OpenAI's GPT-5.6 model family is now generally available on Amazon Bedrock, introducing a three-tier system: Sol (flagship reasoning), Terra (balanced production), and Luna (fast inference). According to OpenAI, Sol scores 80 points on the Artificial Analysis Coding Agent Index and 73.5% on ExploitBench, establishing new benchmarks while using less than half the output tokens of competing models.

Amazon SageMaker AI now supports serverless fine-tuning for NVIDIA Nemotron 3 Nano (30B parameters, 3B active) and Nemotron 3 Super (120B parameters, 12B active). The integration includes supervised fine-tuning, reinforcement learning with verifiable rewards (RLVR), and reinforcement learning from AI feedback (RLAIF).

Amazon SageMaker HyperPod has launched infrastructure updates for enterprise inference workloads. The platform now captures inference data at three points—endpoint, load balancer, and model pod—with configurable sampling and S3 storage. Teams can deploy models directly from Hugging Face Hub without pre-staging weights, with support for gated access across vLLM, TGI, and SGLang runtimes.

Hugging Face and Amazon Web Services have integrated a one-click workflow that takes developers from model discovery on Hugging Face directly into AWS SageMaker Studio. The integration eliminates manual setup steps by automatically provisioning domains with pre-configured IAM permissions and displaying GPU quota availability inline.

Amazon Web Services has added three MiniMax models to Amazon Bedrock: M2, M2.1, and M2.5. The newest model, M2.5, uses a mixture-of-experts architecture with 230 billion total parameters and 10 billion active per token, trained specifically for agent-native execution and coding tasks.

AWS has released infrastructure for deploying multi-turn reinforcement learning to train Amazon Nova models on SageMaker HyperPod. The system requires a minimum of 10 ml.p5.48xlarge instances and costs approximately $786-$1,180 per hour when running.

AWS has released an automated pipeline for redacting personally identifiable information in images, using Amazon Nova 2 Lite as an intelligent coordinator. The solution combines Nova's contextual vision reasoning with Meta's SAM 3 model deployed on SageMaker and Amazon Textract to handle complex PII detection scenarios including faces, fingerprints, ID cards, and license plates.

Amazon Bedrock now supports NVIDIA Nemotron and OpenAI GPT OSS models in AWS GovCloud (US) Regions. The launch includes OpenAI's GPT OSS models (120B and 20B parameters, 128K context) and NVIDIA Nemotron 3 family (9B to 120B parameters, 1M context), providing government agencies FedRAMP High and DoD SRG Level 5-compliant AI inference on U.S. soil.

Amazon has added metadata filtering to its AgentCore Memory service for AI agents. In AWS evaluations across 151 questions, the feature improved overall question-answering accuracy from 40% to 64%, with context-dependent questions jumping from 16% to 69% accuracy. The update allows agents to filter memory retrieval by attributes like priority, department, or time range before semantic search runs.

AWS has published a technical implementation guide for automating healthcare claims processing using Amazon Bedrock Data Automation and Amazon Bedrock AgentCore. The pipeline extracts data from CMS-1500 claim forms, validates against AWS HealthLake records, and generates FHIR-compliant claim resources with automated notifications.

Amazon Web Services launched Web Search on Amazon Bedrock AgentCore, a fully managed web search capability that gives AI agents access to tens of billions of documents without requiring external search APIs. The service, now generally available, runs entirely within AWS infrastructure and refreshes its index within minutes of new content appearing online.

Amazon Web Services made its AgentCore harness generally available, reducing production AI agent deployment to two API calls: CreateHarness and InvokeHarness. The managed service handles sandboxed execution, memory, tool integration, and observability, eliminating infrastructure setup for teams building LLM agents.

Amazon Web Services has released new capabilities for Bedrock AgentCore, its platform for building AI agents. The update includes a managed knowledge base that handles vector storage and retrieval across enterprise data sources, native web search using Amazon's Alexa infrastructure, and a payment system enabling agents to access paid content and APIs.

Amazon Web Services launched the InvokeGuardrailChecks API for Amazon Bedrock Guardrails, enabling developers to apply individual safety checks at any point in agentic AI applications without provisioning guardrail resources. The API operates in detect-only mode and returns numeric scores (0-1 scale) for each safeguard, allowing developers to define custom thresholds and actions in their application logic.

Amazon Web Services today announced availability of Google DeepMind's Gemma 4 family on Amazon Bedrock. The open-weight models include three instruction-tuned variants spanning 2.3B to 30.7B parameters, with 256K context windows, multimodal input support, and built-in reasoning mode.

AWS has launched LangChain Deep Agents, an orchestration framework that spawns isolated subagents using Amazon Bedrock AgentCore infrastructure. AgentCore provides dedicated browser MicroVMs running Chromium via Playwright and full Python environments with pre-installed data science libraries, enabling parallel agent execution without context window competition.

AWS announced Neuron Agentic Development, a collection of AI agents that automate kernel optimization for Trainium and Inferentia chips. The toolkit includes five specialized skills that handle kernel writing, debugging, profiling, and analysis, accessible through coding agents in Kiro and Claude.

Amazon Web Services has launched AgentCore Runtime on Bedrock, providing dedicated Linux microVMs with persistent 14-day storage for coding agents. The service eliminates the need to keep laptops open during agent sessions and supports parallel execution of Claude Code, Codex, Kiro, OpenCode, and other coding agents with isolated environments.

NVIDIA Nemotron 3 Ultra is now available on Amazon SageMaker JumpStart with 550 billion total parameters and 55 billion active parameters. The model features a hybrid Transformer-Mamba Mixture-of-Experts architecture and supports context windows up to 1 million tokens, targeting agentic AI workloads.

OpenAI's GPT-5.5 and GPT-5.4 models are now generally available on Amazon Bedrock, with pricing matching OpenAI's first-party rates. Codex, OpenAI's coding agent used by 5 million developers weekly, is also available with pay-per-token pricing and no seat licenses.

Amazon Web Services released Amazon Bedrock Data Automation (BDA), a foundation model-powered service designed to extract and validate structured data from financial documents. The service uses custom blueprints to process bank statements, W-2 tax forms, 1099-B forms, and vendor contracts, offering what AWS claims is industry-leading accuracy at lower cost than using foundation models directly.

Amazon Nova Act, AWS's browser-based AI agent service, now qualifies as HIPAA eligible, allowing healthcare organizations to deploy autonomous agents for workflows involving electronically protected health information. The service automates repetitive browser tasks including claims processing, referral coordination, and prior authorization.

AWS has launched Amazon Bedrock AgentCore, a serverless runtime environment for deploying production AI agents. Turkish fulfillment company OPLOG demonstrated the platform's capabilities by building three business intelligence agents using Anthropic's Claude Sonnet, achieving a 35% reduction in sales cycles and 98% reduction in manual research time.

AWS has added four multimodal evaluators to its Strands Evals SDK that judge image-to-text AI outputs by directly analyzing source images. The evaluators—Overall Quality, Correctness, Faithfulness, and Instruction Following—use multimodal large language models to detect visual hallucinations, factual errors, and instruction violations that text-only judges miss.