Browsing: applications

Teams shipping AI agents at scale keep running into the same pattern. Even agents built on top-tier models like Fable and Astra struggle to carry out complex, end-to-end tasks inside a specific product consistently enough to deploy. So teams grind out evals for the workflows that matter most, often hundreds or thousands of hand-written cases,…

Voice agents, interactive learning applications, accessibility tools, and customer service assistants need to respond without long silent pauses. In this tutorial, you deploy a text-to-speech (TTS) model on Amazon SageMaker AI that can start playing speech before it finishes generating the full response. You use the AWS vLLM-Omni Deep Learning Container (DLC) to deploy Qwen3-TTS,…

Voice input on phones has been solved for years. What has not been solved is the output. Speak into most dictation tools and you get back exactly what you said, fillers and false starts included, in a note you then have to clean up and move somewhere else. SpeakON attacks that gap with hardware: a…

Wrapping an LLM in a ReAct loop with a few Python tool definitions works fine in a demo sandbox, but deploying it against enterprise infrastructure turns minor hallucinations into cascading outages, ghost database writes, and silent data corruption. The naive approach to agentic automation assumes perfect deterministic tool execution, treats every API call as inherently…