AI

Modernizing and scaling support operations with generative AI on AWS

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Agreements (SLAs), adapting to evolving compliance requirements, and maintaining documentation that quickly becomes outdated, all without proportional increases in headcount.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 […]

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How an AWS team detects dashboard content failures at scale using Amazon Bedrock

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

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From code to diagrams: Agentic architecture documentation with Amazon Bedrock AgentCore

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 Development teams often spend hours manually creating architecture diagrams, only to watch them become outdated within weeks of deployment.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

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Trinity: Agentic AI-powered transition planning for students with disabilities

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:

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System helps humans predict when self-driving cars will make mistakes

Self-driving cars are often controlled by deep learning models that sometimes fail in unexpected situations. For instance, the car might inexplicably brake and block the path of an oncoming emergency vehicle. A human driver or passenger may need to react rapidly to prevent a collision. To help humans better anticipate a vehicle’s mistakes, researchers from

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Walter Torous named executive director of MIT Center for Real Estate

Walter Torous, senior lecturer in the MIT Department of Urban Studies and Planning (DUSP) and the MIT Sloan School of Management, and director of the Master of Science in Real Estate Development Program (MSRED), was recently named executive director of the MIT Center for Real Estate (CRE) — effective July 1, 2026. In announcing Torous’ appointment, School of

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