Amazon, Cursor, Microsoft, OpenAI, and Vercel have released Agent Plugins, an open standard defining a single package format for AI agent extensions. Version 1.0.0 covers Agent Skills and MCP servers, but leaves marketplaces, permissions, and runtime out of scope.
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 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.
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.
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.
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.
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.
AWS has developed Reverse Direct Preference Optimization (rDPO), a novel unlearning technique that reduces over-deflection in Amazon Nova models by up to 53 percentage points. The approach allows organizations to selectively adjust content moderation safeguards while preserving general model capabilities through LoRA adapters.
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.