
Cisco is preparing its infrastructure and software capabilities for enterprises to deploy AI agents as autonomous workplace tools that work alongside humans. The company is addressing two major challenges this creates: AI agents generate significantly more network traffic than traditional tools, and frontier AI models can rapidly discover and generate exploits for software vulnerabilities faster than organizations can patch them. Cisco's response includes a platform called Cloud Control with AgenticOps for managing human-agent collaboration, and a security tool called Live Protect designed to shield systems from newly discovered flaws between patching cycles. The shift from chatbots to autonomous agents integrated into business operations represents a fundamental change in how IT infrastructure and security must be designed and operated.

OpenClaw just released native companion apps for iOS and Android. The iOS app is listed as ‘OpenClaw – AI that does things.’ Both apps are free to download. They are not standalone chatbots. Each phone becomes a node in a self-hosted agent network. The assistant itself runs on a separate Gateway. That separation is the whole design. TL;DR OpenClaw’s iOS and Android apps are companion nodes, not standalone assistants. The Gateway runs the agent; phones add came

In this tutorial, we build an advanced, Colab-ready workflow around PyGraphistry for interactive graph analytics and visualization. We start by creating a realistic enterprise-style access dataset, transforming it into nodes and edges, and enriching the graph with risk scores, anomaly indicators, centrality metrics, community detection, and layout embeddings. We then use PyGraphistry to bind graph structure, visual encodings, labels, tooltips, and filtered subgraphs, and to generate local inter

AI scientists are becoming a new interface for scientific computing. These agents read papers, write code, generate hypotheses, call APIs, and inspect files. But science is not software engineering. No test suite turns green when a hypothesis is correct. Discovery stays iterative, uncertain, and grounded in the physical world. That gap is what NVIDIA is targeting. NVIDIA published a hands-on walkthrough for its BioNeMo Agent Toolkit. The argument is direct. A general coding agent pointed at
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