AI

3 Questions: Beyond data-driven aesthetics

“Beyond Data-Driven Aesthetics,” by MIT Architecture alumnus and researcher Alexandros Haridis, on view at the MIT Keller Gallery through June 30, examines 20th- and 21st-century efforts to transform computing into a medium for creative production and aesthetic judgment in architecture and the applied arts. Drawing on philosophy, mathematics, computer science, and design computation, the exhibition […]

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Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS

At PAR Technology Corporation, we build technology for the restaurant industry, supporting over 300 restaurant businesses, from independent operators to large, multi-brand franchise groups. Across this diverse customer base, we help organizations make better decisions by unlocking the value of their data. When we set out to build a natural language text-to-SQL agent for self-serve

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Build an agentic AI healthcare claims pipeline with Amazon Bedrock and AWS HealthLake

Manually processing paper-based forms remains a significant cost in the healthcare industry. Despite advancements in data extraction of scanned documents and images, human oversight is usually still needed. Entry error by the individual creating the form or lower-confidence extractions from the digitization still must be remediated. In this post, we show you how to build

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Debugging production agents with Amazon Bedrock AgentCore Observability

Production artificial intelligence (AI) agents can fail silently. They may return plausible but incorrect answers, enter infinite reasoning loops, or select the wrong tools without triggering error alerts. These failures make debugging production agent behavior difficult because standard logs and metrics do not capture how decisions are made. Amazon Bedrock AgentCore Observability addresses these debugging

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