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“5 production challenges in multi-agent systems and how Deep Insight solved them”

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5 production challenges in multi-agent systems and how Deep Insight solved them

Many teams build a multi-agent AI prototype in days but hit a wall in production. This post defines five production challenges (orchestration, per-agent model selection, reliable operation, secure code execution, and monitoring) and shows how Deep Insight, an...

Yoonseo Kim · via AWS ML Blog•2 min read

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TechCrunch Founder Summit 2026: Everything you need to know

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Train Qwen3 on AWS Trainium with torchtitan

Train Qwen3 on AWS Trainium with torchtitan three ways on a single trn2.48xlarge: the Hugging Face Transformers v5 modeling backend, torchtitan's native Qwen3, and NKI mixture-of-experts kernels.

Josh Longenecker · via AWS ML Blog•2 min
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Walmart is inviting AI agents to shop for you

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Emma Roth · via The Verge AI•2 min
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How U.S. Hunger connects leaders and data using Amazon Quick

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Rick Whitted · via AWS ML Blog•2 min
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TornadoVM 7 Enables GPU-Native Workloads From Java Applications

TornadoVM, an open-source heterogeneous programming framework for Java has reached version 7.0. It automatically accelerates Java programs on multi-core CPUs and GPUs and provides a rich GPU programming ecosystem in Java that can be used with any mainstream...

Ben Evans · via InfoQ AI/ML•2 min
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Anthropic's cloud partnerships are getting messy

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Work management context for AI agents with Smartsheet MCP on AWS

The Smartsheet Model Context Protocol (MCP) Server lets AI agents read, update, and act on live work management data. This post shows two ways to connect it on AWS: Amazon Quick for business teams and Kiro for developers, so your agents can work with...

Galina Jordanowa · via AWS ML Blog•2 min
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