The agent economy is reshaping financial markets. Open-source agent frameworks are accelerating autonomous financial activity, with AI agents increasingly executing trades, managing portfolios, and interacting directly with exchanges. Yet the financial infrastructure supporting this shift has not evolved at the same pace. CoinQuant, the AI-powered no-code trading platform that has
Today, I’m talking with Google and Alphabet CEO Sundar Pichai, in a conversation we recorded just after the Google I/O developer conference. This is the fifth year Sundar and I have sat down after I/O, and it’s become one of my favorite Decoder traditions.
There’s always a lot of news at I/O, and this year was no exception — Google has powerful new Gemini models, it’s putting AI agents in everything, and it’s making huge changes to Search on both the web and YouTube that will once again reshape the information ecosystem.
That’s a lot to talk about, and Sundar and I got into all of it. But I also realized it’s been a long time since I’d asked Sundar the Decoder questions about structure and decision making, so I started there. You’ll hear Sundar say he realized he needed to rethink how Google worked a few years ago in response to ChatGPT, and he made a lot of executive changes and big decisions to get the company in a more aggressive posture.
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OKX says its new platform can handle up to 300,000 transactions per second, running at millisecond-level speed — a technical benchmark the company set out as it launched Exchange OS on Tuesday. Related Reading: Crypto Payments Go Autonomous As AI Agents Execute 176M Transactions A Shared Home For Different Markets The platform is built on […]
ClickUp CEO Zeb Evans announced last week that the collaboration software company, last valued at $4 billion in 2021, had laid off twenty-two percent of its workforce — framing the cuts not as cost reduction but as a structural shift toward AI-driven operations. The company has deployed roughly 3,000 internal AI agents to handle complex tasks, with […]
The gap between what agentic AI promises and what it actually delivers in live environments is now one of the most consequential engineering problems in the industry. It is also, frustratingly, one that the field has been slow to name precisely, let alone fix...
AI agents look brilliant in a demo because demos are friendly worlds. The data is curated, the tools behave, and nothing important changes while the agent is in mid-thought. Production is the opposite: data arrives late, facts conflict, permissions bite, APIs time out, and the underlying state changes constantly.
That gap is why early “agents in production” often get scoped down to something safer: read-only assistants, human-in-the-loop workflows, or narrow domains with heavily curated data. Several high-profile deployments have also been scaled back after meeting messy real-world constraints. Rather than being a verdict on autonomy, these stumbles are a reminder that autonomy is unforgiving. Small cracks in your data stack become large cracks in agent behavior.
The same pattern shows up whenever agents move from toy workflows to systems with real state. As scope increases, weak guarantees create predictable symptoms: overconfident actions on stale data, brittle reasoning when meaning
Treating AI agents as untrusted systems highlights the need for robust security measures to prevent financial vulnerabilities in crypto transactions.
The post Researchers urge treating AI agents as untrusted systems, warning of crypto security risks appeared first on Crypto Briefing.
Amid the global crackdown on online gambling and prediction markets, Indonesia has joined the list of jurisdictions imposing restrictions on Polymarket and similar platforms after a bet on the President’s term drew online attention. Related Reading: Crypto Payments Go Autonomous As AI Agents Execute 176M Transactions Indonesia Blocks Access To Polymarket Indonesia recently blocked access […]
