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While deploying Model Context Protocol (MCP) servers in production, enterprises need fine-grained access control across servers, observability into which teams use which tools, security guarantees against data exfiltration, and centralized credential management, all at scale. Amazon Bedrock AgentCore Gateway sits between MCP servers and the clients that consume them, centralizing credential management, observability, and secure …

Extending MCP support for Amazon Bedrock AgentCore Gateway Read More »

Securing AI agent behavior is a key customer challenge in building agentic solutions. As enterprises rapidly adopt AI agents to automate workflows, they face a scaling challenge in managing secure access to tools across the organization. Modern unified enterprise AI platforms have hundreds of agents serving users across the organization. These agents need to access …

Secure AI agents with Policy and Lambda interceptors in Amazon Bedrock AgentCore gateway Read More »

Agents increasingly take actions on behalf of their end users, whether that’s selecting tools, browsing the web, and calling MCP servers autonomously to achieve a goal. When the tools, MCP endpoints, or web resources an agent reaches are paid, the agent gets stuck without the ability to transact. Amazon Bedrock AgentCore payments, announced in preview …

Enable safe agentic payments with built-in guardrails using Amazon Bedrock AgentCore payments Read More »

When you build agentic AI solutions, you face unique operational challenges. Agents make unpredictable decisions, costs spiral unexpectedly, and debugging non-deterministic failures seems impossible. Agentic AI applications don’t just execute predetermined workflows. They reason, adapt, and make autonomous decisions, and DevOps practices need to be adapted. That’s where AgentOps comes in, the operational discipline for …

AgentOps: Operationalize agentic AI at scale with Amazon Bedrock AgentCore Read More »

If you’re iterating on deploying large language models (LLMs) on AWS GPU instances, you’ve probably noticed the larger the model to be loaded into GPU High Bandwidth Memory (HBM), the longer the painful wait until the GPUs are ready for inference. As models grow to hundreds of billions of parameters and GPU environments grow ever …

Accelerate LLM model loading and increase context windows with GPUDirect on Amazon FSx for Lustre and TurboQuant Read More »

Model Context Protocol (MCP) integration in Amazon Quick transforms how financial analysts access time-series market intelligence, removing the need for complex database queries. As a financial analyst, you navigate millions of stock trades flowing through markets every second, searching for patterns that drive trading decisions. Financial institutions often use time series databases to analyze high-frequency …

Amazon Quick integration with time-series databases for market intelligence using MCP Read More »

Learn how Googlers used AI to produce Google I/O 2026. ​ 

Deploying large language models (LLMs) at scale on Amazon SageMaker AI Inference makes observability a critical pillar of any production machine learning (ML) strategy. Unlike conventional software that returns deterministic outputs, LLMs generate variable, free-form responses that are difficult to validate with standard metrics. LLM output quality can change over time as input distributions shift, …

Comprehensive observability for Amazon SageMaker AI LLM inference: From GPU utilization to LLM quality Read More »

We used Google AI Studio to vibe code a quiz about our top I/O 2026 announcements. ​ 

Watch 11 videos showing the capabilities of Gemini Omni and Gemini 3.5, announced at Google I/O 2026. ​