Amazon Web Services

After talking with enterprise security leaders over the past year, one thing has become clear: the rise of autonomous AI agents is the most significant shift in security posture since the move to cloud. Organizations across every industry are adopting AI agents that authenticate on behalf of users, execute multistep workflows, and make decisions across infrastructure, often without waiting for human approval. Security operations need to keep pace.

Building a Medallion Architecture today typically means that you must build three separate systems working in concert: extract, transform, and load (ETL) jobs to transform data between layers, an orchestrator (such as Apache Airflow or AWS Step Functions) to sequence those jobs in the correct order, and custom change-data-capture (CDC) logic to make sure that each job processes only new or modified records.

Handling upstream schema changes is a common operational challenge in streaming data pipelines that write to a data lake. When a source schema changes, teams often face a difficult choice: restart the pipeline or perform a manual migration. A restart can pause ingestion and delay or lose in-flight data. A manual migration consumes engineering time and introduces the risk of schema inconsistencies while the data lake falls behind the source.

Scaling support operations requires handling rising ticket volumes, meeting strict Service Level Agreements (SLAs), adapting to evolving compliance requirements, and maintaining documentation that quickly becomes outdated, all without proportional increases in headcount. In many teams, the knowledge required to resolve tickets is fragmented across SOPs, recordings, and tribal expertise, forcing analysts to spend significant time searching for guidance instead of resolving issues.

Picture a scenario familiar to any organization running business intelligence (BI) at scale: A user opens a dashboard minutes before an important meeting and finds a blank chart. Every infrastructure monitor reports healthy. Servers are up, APIs respond, and the data pipeline completed on schedule. Yet the content on screen is broken, and no monitoring system has flagged it. This class of failure is inherently silent: It exists only in what the user sees.

Architecture documentation remains one of the most persistent challenges in software development as code bases evolve rapidly. Development teams often spend hours manually creating architecture diagrams, only to watch them become outdated within weeks of deployment. This documentation gap creates knowledge silos, slows developer onboarding, and complicates compliance audits. Amazon Bedrock AgentCore is the platform to build, connect, and optimize agents at scale with any framework or model.

This post was co-authored with Marc Steren, Odina Salihbaeva, and Aashrit Surapaneni from University Startups, a partnership between University Startups, g/d/n/a, and AWS. Trinity is a conversational AI solution that helps students with disabilities take ownership of their postsecondary planning. It was developed by University Startups, which was founded in 2020 on a straightforward belief: students with disabilities deserve a clear, personalized path forward.

For the 7 million residents of the San Francisco Bay Area, getting to work, school, or home safely and on time depends on reliable, real-time travel information. Whether it’s checking when the next bus, train, or ferry departs; finding out about a freeway closure; or getting a broken-down car towed off the Bay Bridge, the region’s 511 traveler information service is a critical public resource. Its phone service runs on Amazon Web Services (AWS) using Amazon Connect Customer and Amazon Lex.

Private offer auto-renewals are now live in AWS Marketplace, in which a buyer and seller agree to the original offer terms and future auto-renewal terms one time, at offer creation. Each agreement then renews automatically without rebuilding offers or chasing approvals, with opt-out rights for both parties. Most renewals aren’t a fresh decision: the buyer relies on the service and wants to keep it running, and the seller wants to retain and grow the account.

Explore and use Amazon Simple Storage Service (Amazon S3) like a file system with the new S3 PowerShell Drive. You can mount your S3 storage as a drive and navigate it with the same commands that are frequently used for local files: Set-Location, Get-ChildItem, Get-Content, Set-Content, and Remove-Item. Browse buckets and prefixes, read and write objects, and clean up data without needing to compose individual S3 cmdlet calls.