271 summarised stories about Agentic Workflows, each linking back to the original source. Browse all topics →
OpenAI 2 weeks ago 17
The article argues that advanced AI’s biggest impact will come from routine execution work that supports breakthrough ideas. No specific figures or dates are provided. It suggests progress and the “next economy” will be shaped by how well execution is handled rather than by discoveries alone.
Amazon Web Services 2 weeks ago 16 ● 2 sources
Amazon Bedrock AgentCore provides a way to build ambient agents that start jobs from event streams like S3 uploads and then pause for human input before continuing. The reference design caps each agent turn at the AWS Lambda 15-minute timeout. This shifts agent execution from chat-based, user-initiated prompts to serverless, event-driven workflows with a single ask_human interaction that routes reviews through a Jobs interface.
Amazon Science 2 weeks ago 44
Graph intelligence research and production systems were advanced from passive network topology modeling to agentic reasoning over graph-structured digital twins for root cause analysis. The approach was demonstrated with NTT DOCOMO at Mobile World Conference earlier this year, achieving root cause identification in minutes on commercial networks. This shifts NOC workflows toward a cascaded, graph-centric analytics pipeline with an agentic layer that selects algorithms, computes rankings relative to the incident, and updates via feedback.
Product Hunt 2 weeks ago 48
Finbar launched public availability of its investment research platform for investors and their agents, targeting AI-assisted research workflows. The announcement says it is the platform’s 2nd launch. As a result, its worldwide fundamental data and AI research toolkit are now offered publicly, including access via API/MCP and use in web or Excel.
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21 October 2026
AI’s big throughline today wasn’t a new model so much as the growing paperwork required to make models usable in the real world. Nvidia used its GTC Berlin keynote to pitch DSX as a packaged “AI factory,” with each DSX POD containing seven Vera Rubin NVL72 racks and aiming to compress deployment from six months to as little as 14 days. The message is clear: enterprises don’t just need GPUs; they need reference architectures, sandboxed agent runtime, and deployment plans that play nicely with security and data readiness. DSX also leans on OpenShell-style tooling for running agents with isolated monitoring—because “agent” still sounds like magic until you have to explain how it behaves when something crashes.
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