News For Dev-ops

Amazon SageMaker Unified Studio CI/CD expands its open-source deployment toolkit with two new capabilities: (1) an AI agent skill that automates manifest authoring, and (2) native notebook promotion across environments. Together, they help data teams go from project to production faster while maintaining best-practice defaults across stages. AI-assisted manifest generation. The new generate-bundle-manifest agent skill inspects a project's connections, storage, and workflows and produces a ready-to-use deployment manifest. It applies least-privilege IAM guidance, substitutes environment variables in place of hardcoded resource identifiers, and sets safe defaults such as opt-in catalog handling. Teams can import the skill into their own agents to standardize how they package and promote SageMaker Unified Studio projects across development, test, and production accounts. Native notebook promotion. The CI/CD toolkit now supports promoting native SMUS Notebooks alongside code, workf...

AI coding agents can build SharePoint Framework solutions. But should you trust them to choose the right toolchain and package versions by themselves, then apply every upgrade step correctly? We wanted evidence. Before publishing SPFx Dev Skills , the SharePoint Framework team and Developer Relations worked together to understand what agents already know and where they struggle. We then measured which guidance improves the outcome. It’s easy to package our expertise in a skill and tell developers to trust it. Proving where the skill adds value takes more work, but produces a far more useful result. We started without the skill The collaboration brought together Vesa Juvonen and Bert Jansen from the OneDrive and SharePoint SPFx team, and Garry Trinder and Waldek Mastykarz from Developer Relations. Collectively, we defined what a correct upgrade should look like and designed the evaluations, then traced failures through the agents’ complete sequence of actions. Together, we turned tho...

Recall that Microspeak is not merely for jargon exclusive to Microsoft, but it's jargon that you need to know to survive at Microsoft. In business, funding usually refers to having enough money to pay for ongoing operations. In Microsoft engineering groups, it doesn't mean that. In Microspeak, funding refers to having enough people available to work on a feature. A feature that has adequate people assigned to work on it is considered to be funded , or for emphasis, fully funded , whereas a feature that does not have people working on it is unfunded . Of course, you can have intermediate states, like partially funded , to say that some people have been assigned to it, but not enough to finish the work in time for a specific requested completion date. Here are some citations I found. Funding for this feature is being worked through. This is another way of saying, "We are working on finding people to work on this feature." This might come at the cost of defundi...

Amazon Kinesis Data Streams now supports a dry run feature to check whether an API request would succeed without executing the operation. Customers can now set the new optional parameter ‘DryRun’ to true in their API requests to validate permissions before interacting with a stream in production. Previously, customers had no safe way to test whether their application had the correct permissions to access a stream. They would often send a request engineered to fail after checking permissions, such as a PutRecord request with a payload deliberately larger than the maximum supported size. This approach was fragile as it depended on current service limits, and if those limits ever changed, the request could unexpectedly succeed, writing unintended records into the production stream and into any downstream consumers. Now, customers can simply set the parameter ‘DryRun’ to true to indicate they only want to validate the API request. If all checks complete successfully, the API returns a ‘D...

Amazon Kinesis Data Streams introduces streaming tables, a new fully serverless capability that continuously delivers data from Kinesis streams to Amazon S3 Tables in Apache Iceberg format. Streaming tables eliminate the need to build and operate self-managed Iceberg delivery pipelines, reducing data delivery costs by up to 50%. Intelligent inline compaction eliminates the small file problem and keeps query performance predictable, reducing downstream query costs by up to 30%. Amazon Kinesis Data Streams is a serverless streaming data service that makes it easy to capture, process, and store data streams at any scale. Customers increasingly want to deliver streaming data to Apache Iceberg tables to power near real time analytics and AI/ML feature pipelines using the freshest data. To do this today, customers build and manage custom pipelines, handle format conversions, and manage compute infrastructure, adding to their costs. High volume streaming ingestion also creates many small Pa...

Explore how the VS Code Agent Host and AHP support multiple agent harnesses with durable, synchronized local and remote sessions. Read the full article Post Updated on August 26, 2026 at 01:00AM Thanks for reading from devamazonaws.blogspot.com