"NUNTII EX MACHINA"
DOCUMENTING THE RACE TO AGI

THE
AGENTIC
TIMES_

▮ NEWS_TICKER.LIVE REC
▸ Ryan Serhant Reveals How He Turns AI Agents Into Million-Dollar Sales Forbes · 21.07.2026▸ Chinese Tech Firms Pitch AI Agents as the Future of Smartphones citynewsservice.cn · 21.07.2026▸ Powerful AI Models Happily Being Given Away for Free RealClearMarkets · 21.07.2026▸ GitLab 19.2 Puts AI Agents to Work on the Security Backlog infoq.com · 21.07.2026▸ OpenAI’s GPT-Red: How AI Models Are Now Training Each Other to Be More Secure quasa.io · 21.07.2026▸ MCP update prepares AI agents for widespread deployment Techzine Global · 21.07.2026▸ New ENCFORGE Ransomware Targets AI Model Files in Langflow RCE Attack The Hacker News · 21.07.2026▸ In Depth: Chinese Smartphone-Makers Bet on AI Agents Caixin Global · 21.07.2026▸ WebMCP: Bridging the Gap Between AI Agents and the Web devmio · 21.07.2026▸ Boomi study finds AI agent trust lags enterprise adoption InsiderPH · 21.07.2026▸ Rapidus and Cadence partner to advance AI-driven SoC design New Electronics · 21.07.2026▸ Agentic AI and Smart Data: The Architecture of the UK’s New Open Finance Framework… Finextra Research · 21.07.2026▸ Databricks: US$188bn Valuation, Genie One and Agentic AI AI Magazine · 21.07.2026▸ Nvidia targets simulation bottlenecks with AI agent expansion InsiderPH · 21.07.2026▸ China Weighs Export Controls on AI Models, Including Open Weight LLMs trendingtopics.eu · 21.07.2026▸ Research says JadePuffer Ransomware wipes off data on AI Model Infrastructure Cybersecurity Insiders · 21.07.2026▸ Reported US push to ban Chinese AI models reflects anxiety over eroding tech hegemony… Global Times · 21.07.2026▸ Webinar: Can AI agents finally automate data testing? QA Financial · 21.07.2026
▮ MODEL_FEED.LIVE REC
▸ Nemotron-Labs-Audex-30B-A3B NVIDIA · 30B · MoE · 06.07.2026▸ Nemotron-Labs-Audex-2B NVIDIA · 2B · 06.07.2026▸ DeepSeek-V4-Flash-DSpark DeepSeek · Open language model · 27.06.2026▸ Qwen-AgentWorld-35B-A3B Qwen · 35B · MoE · multimodal · 22.06.2026▸ GLM-5.2 Zhipu · Open language model · 16.06.2026▸ North-Mini-Code-1.0 Cohere · coding · 05.06.2026▸ DeepSeek-V4-Pro DeepSeek · Open language model · 22.04.2026▸ DeepSeek-V4-Flash DeepSeek · Open language model · 22.04.2026▸ granite-4.1-8b IBM · 8B · 06.04.2026▸ granite-4.1-3b IBM · 3B · 06.04.2026▸ Mamba2-primed-HQwen3-8B-Instruct Amazon · 8B · instruct · 31.03.2026▸ Falcon-OCR TII · Open language model · 22.02.2026▸ tiny-aya-base Cohere · Open language model · 13.02.2026▸ tiny-aya-global Cohere · Open language model · 13.02.2026▸ GLM-4.7-Flash Zhipu · Open language model · 19.01.2026▸ Falcon-H1R-7B TII · 7B · 29.10.2025
▮ DISPATCHES / 16.04.2026 · 2 MIN READ

Visa and Mastercard Are Building the Rails for Agentic Commerce

Visa and Mastercard Are Building the Rails for Agentic Commerce

When Visa and Mastercard move in the same direction at the same time, the payments industry listens. In early April both networks announced significant agentic AI expansions that signal a fundamental shift in how transactions will be authorised and executed in the near future.

Visa deployed AI to handle payment disputes and automate corporate bill payments through a new partnership with Ramp. Mastercard, meanwhile, expanded its agentic payments network to Hong Kong, part of a stated ambition to build international infrastructure specifically designed for AI-to-AI commerce — transactions initiated and completed by agents acting on behalf of humans.

That last phrase deserves to sit with you for a moment. AI-to-AI commerce. Not a human instructing an AI to make a purchase. Two AI systems negotiating, authorising, and settling a transaction between themselves.

For business leaders the practical implications are immediate. Expense management, procurement, and supplier payments are the obvious first targets — workflows where the decision rules are clear enough to delegate entirely to an agent. Both Visa and Mastercard are betting that enterprises will want this capability at scale, and they are building the infrastructure now.

The deeper question is one of trust and liability. When an agent makes a purchase that turns out to be fraudulent, erroneous, or simply wrong — who bears responsibility? The card networks are positioning themselves as the answer to that question, embedding governance and dispute resolution directly into the agentic payment layer.

The race to own the financial infrastructure of the agentic economy has quietly begun.

END OF TRANSMISSION ▮ ◂ MORE DISPATCHES