Blog_dumb

Generative AI has rapidly evolved from experimental prototypes into systems that are expected to operate reliably in production, at scale, and under real-world performance constraints. As organizations move beyond demos and proofs of concept, they increasingly encounter challenges related to inference latency, scalability, state management, and operational visibility. Building high-performance AI agents today requires more …

Build highly scalable serverless LangGraph multi-agent systems in AWS with Amazon Bedrock AgentCore Read More »

Building high-performance generative AI agents requires architecture that can deliver fast inference, coordinate multiple agents, and operate reliably under production workloads. If you are building generative AI agents to automate reviews, power digital assistants, and support complex decision-making workflows, you need these agents to perform well. They must reduce manual effort, respond in near real …

Build high-performance generative AI systems with Strands Agents, NVIDIA NIM, and Amazon Bedrock AgentCore Read More »

AgentWatch delivers ambient AWS resource monitoring for your DevOps team, moving beyond the reactive cycle of managing Amazon CloudWatch alarms across multiple accounts. CloudWatch alarms trigger too late, AWS Lambda errors accumulate unnoticed, and Amazon Elastic Compute Cloud (Amazon EC2) performance degradation goes undetected until customers report problems. This leaves your team constantly firefighting rather …

AgentWatch: Proactive AWS monitoring with ambient agents Read More »

Building an AI app shouldn’t require a PhD in machine learning (ML) or months of wrestling with complex architectures. Yet that’s exactly what happens when you try to orchestrate multiple API calls, manage conversation state, and create agents that can reason on their own. I’ve seen straightforward AI ideas balloon into sprawling projects that demand …

From idea to AI app: Creating intelligent research assistants with Strands Read More »

When hundreds to thousands of users are onboarded to an enterprise AI platform, business leaders and platform owners need visibility into who is using the platform, whether users are satisfied with the answers they receive, and which capabilities are driving the most engagement. Without a centralized observability solution, this data is scattered across multiple AWS …

Build an enterprise observability solution for Amazon Quick Read More »

In this post, we explore how the Amazon Quick document and visualization creation capabilities work, what you can build with them, and how professionals across roles are using them to reclaim hours of their workweek. From technical execution to strategic judgment Most professional roles carry an unspoken assumption that a significant portion of your time …

Transforming professional work: How Amazon Quick turns document creation from hours into minutes Read More »

A recap of the 2026 I/O Dialogues, where leaders discuss the future of AI, quantum computing, robotics and creativity. ​ 

Healthcare and life sciences (HCLS) organizations depend on repetitive, manual browser-based tasks for critical workflows like claims processing and referral coordination. While agentic AI can automate these workflows, compliance requirements under the Health Insurance Portability and Accountability Act (HIPAA) have limited adoption where electronically protected health information (ePHI) might be present.Amazon Nova Act now qualifies …

Amazon Nova Act is now HIPAA eligible Read More »

Many healthcare organizations report that traditional worklist systems rely on rigid rules that ignore critical context, radiologist specialization, current workload, fatigue levels, and case complexity. This creates a persistent challenge: radiologists cherry-pick easier, higher-value cases while avoiding complex studies, leading to diagnostic delays and increased costs. Research across 62 hospitals analyzing 2.2 million studies found …

Intelligent radiology workflow optimization with AI agents Read More »

As your AWS infrastructure scales, operational workflows naturally grow more complex. SREs and DevOps Engineers spend significant time context-switching between the AWS Management Console, CLI documentation, and multiple service dashboards. They manually translate business questions into the correct API syntax, chain calls across services, and rebuild the same integration patterns for each new use case.This …

Integrating AWS API MCP Server with Amazon Quick using Amazon Bedrock AgentCore Runtime Read More »