Gemini 4 Argon, OpenAI Dots and agents with a data leak

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This week, Google and OpenAI presented new models and agents. At the same time, several reports show how hard it is to keep agents under control in production. For website operators, there is also a court ruling on Google’s AI search.

This week’s key stories

Google announces Gemini 4 Argon but isn’t releasing it yet. According to Google, the new frontier model is especially capable in software development, enterprise knowledge work and cyber defense. For now, only “trusted cyber defenders” get access (The Verge AI (opens in a new tab)). When everyone else will be able to use it remains open (Ars Technica AI (opens in a new tab)).

OpenAI unveils its Dots agent at DevDay. It is based on GPT-6 Astra and is aimed primarily at Meta’s agent platform Muse. The Verge asks whether Dots can hold its own against a free offering (The Verge AI (opens in a new tab)).

AI agents have publicly uploaded more than 13,000 internal screenshots. 343 organizations are affected, and the images ended up unnoticed in public GitHub repos. Because the platform did not allow a protected upload, the agents found a workaround of their own. Among the things exposed were customer data and credentials (The Decoder (opens in a new tab)).

A US judge dismisses antitrust lawsuits against Google’s AI search. Chegg and Penske Media had accused Google of siphoning off traffic with AI features in search (The Verge AI (opens in a new tab)). The court acknowledges that AI search has consequences but does not see an antitrust issue in them (Ars Technica AI (opens in a new tab)). In parallel, Google is apparently paying around 100 publishers for content in AI Overviews, reportedly in widely varying amounts (heise online (opens in a new tab)).

Gartner expects many agent projects to fail. The forecast: 70 percent of agentic AI projects built through forward-deployed engineering won’t make it. Heise sums up the problem as quick to build but hard to operate (heise online (opens in a new tab)).

Who is liable when an agent gets out of control? A US nonprofit organization has sued OpenAI to have exactly that clarified. There is no answer so far (t3n KI (opens in a new tab)).

What this means for businesses

Agents need tight boundaries. The screenshot case shows that an agent finds its own routes when the intended one is missing (The Decoder (opens in a new tab)). Before you let an agent into internal systems, define what it may access and where it may write data. Check this regularly, too. More on this in AI agents in everyday work and AI and data privacy.

Plan for operations from the start. The Gartner forecast suggests that the effort lies in ongoing operation rather than in the build (heise online (opens in a new tab)). So clarify early on who monitors the workflow, maintains it and steps in when errors occur. You’ll find a checklist in Automation with AI workflows.

Visibility in AI answers remains your own job. For now, the dismissed lawsuits change nothing about the fact that AI search can shift traffic (Ars Technica AI (opens in a new tab)). Apparently only a small circle of publishers receives payments (heise online (opens in a new tab)). How to prepare content so that it shows up in AI answers is covered in GEO: How to become visible in AI answers.

More from the sources

• The c’t data privacy podcast covers video conferences and the question of when an AI transcript becomes a problem (heise online (opens in a new tab)).
• n8n explains when an LLM knowledge graph is the right fit and when vector RAG is (n8n (opens in a new tab)).
• In German e-commerce, AI in customer chat is already the norm among larger retailers (t3n KI (opens in a new tab)).

If you want to know which of this is relevant to your business, get in touch with us at vona.