#aws

#aws

When the company reported Q3 earnings on Oct. 30, it beat on the top and bottom lines, with EPS of $1.95 vs. an estimated $15.7, and revenue of $180.17 vs. $177.80 estimated. Meanwhile, revenue from Amazon Web Services was $33 billion and revenue from advertising was $17.7 billion. Concerns about the company's enormous AI CapEx remain, but after the Q3 earnings call, the stock was rewarded by bullish investor sentiment, hitting its first record high since February 2025.

Imagine asking your AWS infrastructure a question in plain English and getting an intelligent, contextual answer - not raw JSON or CloudWatch graphs, but actual insights. That's what becomes possible when you combine the Model Context Protocol (MCP) with AWS. I recently built CloudWhisper, an AI-powered chatbot that uses MCP to connect AI models like ChatGPT and Claude directly to AWS services. In this article, I'll explain how MCP works with AWS, why it matters, and how to build your own MCP-powered AWS integration.

Unified observability signifies the possession of one consistent view over all the parts of a distributed system - applications, infrastructure, and services. With modern systems being spread over several clouds, microservices, and APIs, the only way to have such visibility is through open standards and distributed tracing. Standards such as OpenTelemetry not only make it easier to collect but also to correlate the telemetry data (logs, metrics, and traces) from various components.

Karrot, a leading platform for building local communities in Korea, uses a recommendation system to provide users with personalized content on the home screen. The system consists of the recommendation machine learning model and a feature platform that acts as a data store for users' behaviour history and article information. As the company has been evolving the recommendation system over recent years, it became apparent that adding new functionality was becoming challenging, and the system began to suffer from limited scalability and poor data quality

The three agent offerings, dubbed frontier agents, are "a new class of AI agents that are autonomous, scalable, and work for hours or days without intervention," stated AWS in a press release.

Today, we're announcing Amazon Route 53 Accelerated recovery for managing public DNS records, a new Domain Name Service (DNS) business continuity feature that is designed to provide a 60-minute recovery time objective (RTO) during service disruptions in the US East (N. Virginia) AWS Region," said Micah Walter, senior solutions architect, in a post on the .

However, AWS claims that during its internal use of Strands Agents, its developers encountered issues while deploying agents built with the SDK. The SDK's reliance on model-driven reasoning, according to AWS, often produced unpredictable outcomes once agents hit production workloads, leading to inconsistent results, misinterpreted instructions, and high-maintenance prompt engineering - all of which were impediments to adoption at scale.

Dynatrace connects its observability platform to Amazon Bedrock AgentCore. The integration provides real-time insight into autonomous AI agents within AWS environments. For developers, this means better control over agentic workflows and their performance. Amazon Bedrock AgentCore helps build and deploy AI agents without requiring infrastructure management. The integration with Dynatrace ensures that agent telemetry is converted into insights. Teams can use this to monitor the reliability and responsiveness of agents at the trace level. Intelligent alerts for key metrics become possible.

Instead of navigating AWS Console or writing scripts, just ask: Example Queries: "What are the IP addresses of my EC2 instances?" → Gets all running instances with public/private IPs "How much storage am I using in S3?" → Lists all buckets with sizes and costs "Show me my CloudWatch alarms" → Displays all alarms with status and metrics "Are there any security issues?" → AI-powered security analysis with recommendations

The DNS Planner continued to generate new plans, and while the first Enactor was delayed, a second DNS Enactor began to implement those plans. Once the second Enactor had implemented the new plans, it began the clean-up process, which deletes all plans older than the one it had just implemented. At the same time, the first Enactor finally caught up and overwrote the newer plan, with a check that should have prevented that from happening also failing due to the high delays.