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Google tests Remy AI agent for Gemini as focus turns to user control

Google is testing Remy, a new AI personal agent for Gemini, according to Business Insider. The tool is designed to take actions for users in work and daily tasks. Remy is being tested in a staff-only version of the Gemini app. The report said it reviewed an internal document and spoke with two people familiar […]

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Google made agentic AI governance a product. Enterprises still have to catch up.

Two weeks ago at Google Cloud Next ’26 in Las Vegas, Google did something the enterprise AI industry has been dancing around for the better part of two years: it made agentic AI governance a native product feature, not an afterthought. The centrepiece announcement was the Gemini Enterprise Agent Platform, pitched as the successor to Vertex AI

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What LG and NVIDIA’s talks reveal about the future of physical AI

LG is currently engaged in exploratory discussions with NVIDIA concerning physical AI, data centres, and mobility. Following a meeting in Seoul between LG CEO Ryu Jae-cheol and Madison Huang, Senior Director of Product Marketing for Omniverse and Robotics at NVIDIA, the core operational dependencies required to run complex automated systems are becoming apparent. While the

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IBM launches AI platform Bob to regulate SDLC costs

To regulate software delivery costs and SDLC governance, IBM is launching Bob, an AI platform built to anchor enterprise engineering. Accumulated technical debt, hybrid cloud structures, and rigid compliance requirements clash with the raw speed of coding assistants. Without boundaries, they generate unmanaged liabilities rather than functional progress. Dinesh Nirmal, SVP at IBM Software, explained:

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Kakao Mobility details Level 4 autonomous driving roadmap for physical AI

Kakao Mobility has set out plans to develop Level 4 autonomous driving technologies in-house as part of its physical AI strategy. Kim Jin-kyu, vice president and head of Kakao Mobility’s Physical AI division, presented the roadmap at the 2026 World IT Show conference at COEX in Seoul. His session focused on autonomous driving services built

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NVIDIA and Google infrastructure cuts AI inference costs

At the Google Cloud Next conference, Google and NVIDIA outlined their hardware roadmap designed to address the cost of AI inference at scale. The companies detailed the new A5X bare-metal instances, which run on NVIDIA Vera Rubin NVL72 rack-scale systems. Through hardware and software codesign, this architecture aims to deliver up to ten times lower

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Sony AI robot beats players as humanoid robot wins Beijing race

An autonomous table tennis robot developed by Sony AI has competed against and defeated high-level human players in regulated matches, according to Reuters. The system is part of a broader category often referred to as “physical AI,” where artificial intelligence is applied to machines operating in real-world environments. The robot, named Ace, was designed to

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Reversing enterprise security costs with AI vulnerability discovery

Automated AI vulnerability discovery is reversing the enterprise security costs that traditionally favour attackers. Bringing exploits to zero was once viewed as an unrealistic goal. The prevailing operational doctrine aimed to make attacks so expensive that only adversaries with functionally unlimited budgets could afford them, thereby disincentivising casual use. However, the recent evaluation by the

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Siemens introduces AI system for automation engineering

Siemens has introduced the Eigen Engineering Agent, an AI system designed to plan and validate automation engineering tasks in operational environments. The system uses multi-step reasoning and self-correction to carry out tasks autonomously and operates directly inside engineering platforms, letting it to complete workflows from initial design through to validation. Autonomous engineering workflows The agent

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OpenAI Agents SDK improves governance with sandbox execution

OpenAI is introducing sandbox execution that allows enterprise governance teams to deploy automated workflows with controlled risk. Teams taking systems from prototype to production have faced difficult architectural compromises regarding where their operations occurred. Using model-agnostic frameworks offered initial flexibility but failed to fully utilise the capabilities of frontier models. Model-provider SDKs remained closer to

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