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Curated links from external sources — not 360Softy original articles.

ExternalAI
NVIDIA Technical Blog

Scaling Agentic AI Factories Through Extreme Co-Design with NVIDIA BlueField

Agentic AI changes the infrastructure pattern for AI factories. One request can trigger many model calls, tool calls, memory lookups, policy checks, storage... Agentic AI changes the infrastructure pattern for AI factories. One request can trigger many model calls, tool calls, memory lookups, policy checks, storage accesses, and network transfers before a final answer is produced. As more agents run at once and carry context across steps, users, tools, services, and sessions, infrastructure must

NVIDIA Technical BlogRead original
ExternalCybersecurity
Microsoft Security Blog

Least privilege for AI agents: Identity, access, and tool binding

As AI agents become more autonomous, strong identity, access, and auditing controls are critical to keeping them secure. The post Least privilege for AI agents: Identity, access, and tool binding appeared first on Microsoft Security Blog.

Microsoft Security BlogRead original
ExternalCloud
Google Cloud Blog

Cloud CISO Perspectives: How AI leverages deep context as the defender’s advantage

Welcome to the first Cloud CISO Perspectives for July 2026. Today, Francis deSouza, COO, Google Cloud and President, Security Products, explains the crucial role that deep context plays in creating an AI advantage for defenders. As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog. If you’re reading this on the website and you’d like to receive the email version, you can subscribe here. aside_block <ListValue: [StructValue([('title',

Cloud CISOAI & Machine LearningSecurity & Identity
Google Cloud BlogRead original
ExternalCloud
Google Cloud Blog

Bridging the gap between SQL and Python with BigQuery and the %%bqsql magic

Data scientists and data engineers often find themselves caught between two worlds: SQL and Python. Some find SQL more intuitive, especially when combined with a powerful engine like BigQuery to process data at scale. Others find it easier to work in Python with its rich ecosystem of libraries and runtimes. Historically, using these languages together in one notebook required moving data from SQL results to in-memory and writing from Python memory to temporary tables for SQL to access. To solve

Data Analytics
Google Cloud BlogRead original
ExternalCloud
Google Cloud Blog

What 10 autonomous film crews taught us about agent teamwork

Can teams of AI agents collaborate to create a short film? As part of an internal Google generative media hackathon, we put this question to the test – specifically, to uncover whether AI agents could work collaboratively in a domain less innately familiar than software development. We gave each crew three agents with distinct roles and had them collaborate through messages and shared files under their own agent-only hackathon. Agents ran inside Scion, an open source agent orchestration testbed.

Developers & Practitioners
Google Cloud BlogRead original
ExternalCloud
Google Cloud Blog

Three lessons in accelerating foundation model upgrades

Have you run into problems migrating your products from one model to the next? Upgrading to the latest AI models is rarely simple. For engineering teams, model updates whether migrating to an entirely new model or updating to a newer checkpoint within the same model family, like moving from an earlier Gemini version to Gemini 3.5 — often require a slow and costly process of testing, proving quality, and manually evaluating new responses. For most engineering teams, upgrading to a new model check

AI & Machine LearningCompute
Google Cloud BlogRead original

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