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File Search is a fully managed Retrieval Augmented Generation (RAG) system built directly into the Gemini API. ​ 

As AI agents are adopted at scale, developer teams can create dozens to hundreds of specialized Model Context Protocol (MCP) servers, tailored for specific agent use case and domain, organization functions or teams. Organizations also need to integrate their own existing MCP servers or open source MCP servers for their AI workflows. There is a

Learn more about the new Google Finance, including new features like Deep Search and prediction markets data. ​ 

How Google approaches AI and education, from our tools to our commitment to responsibility. ​ 

In this post, we show you how Amazon Search optimized GPU instance utilization by leveraging AWS Batch for SageMaker Training jobs. This managed solution enabled us to orchestrate machine learning (ML) training workloads on GPU-accelerated instance families like P5, P4, and others. We will also provide a step-by-step walkthrough of the use case implementation. Machine

Today, we’re announcing enhancements to Structured Outputs in the Gemini API. ​ 

Amazon Bedrock AgentCore is an agentic platform for building, deploying, and operating effective agents securely at scale. Amazon Bedrock AgentCore Runtime is a fully managed service of Bedrock AgentCore, which provides low latency serverless environments to deploy agents and tools. It provides session isolation, supports multiple agent frameworks including popular open-source frameworks, and handles multimodal

Here are Google’s latest AI updates from October 2025 ​ 

In high-volume healthcare contact centers, every patient conversation carries both clinical and operational significance, making accurate real-time transcription necessary for automated workflows. Accurate, instant transcription enables intelligent automation without sacrificing clarity or care, so that teams can automate electronic medical record (EMR) record matching, streamline workflows, and eliminate manual data entry. By removing routine process

Enterprises in regulated industries often need mathematical certainty that every AI response complies with established policies and domain knowledge. Regulated industries can’t use traditional quality assurance methods that test only a statistical sample of AI outputs and make probabilistic assertions about compliance. When we launched Automated Reasoning checks in Amazon Bedrock Guardrails in preview at