Key Points
Kraken rebuilds app around agentic AI that monitors markets and recommends trades continuously.
Users set financial goals and risk tolerance; AI builds draft portfolios for review and approval.
Every trade requires user confirmation before execution; no autonomous trading without permission.
Crypto exchanges including Coinbase and Gemini are embedding AI tools to compete on personalized investing features.
Kraken is completely rebuilding its mobile app around agentic AI trading, the company told CNBC on July 10. Users will set financial goals and risk preferences, then AI agents will continuously monitor markets, identify opportunities and recommend trades in real time. Every trade requires user approval before execution. Kamo Asatryan, Kraken’s chief data officer, said the goal is to give everyday investors the same market awareness as professional traders, using plain English instead of complex tools.
How the new Kraken app will work
The redesigned app uses AI to learn each user’s financial goals, risk tolerance, funding preferences and financial profile during onboarding. Based on that information, the AI builds a draft portfolio for users to review and adjust before investing. Once live, the app provides personalized portfolio updates and investment suggestions tailored to each user’s holdings, monitoring markets around the clock.
Why Kraken is moving beyond crypto trading
Kraken said most trading platforms hand users powerful but complex tools and leave them to figure out how to use them alone. The company believes AI can bridge the gap between everyday investors and professional traders. The overhaul reflects a broader shift toward AI-native financial products as large language models reshape how investors interact with markets. Kraken is expanding into payments, banking and lending alongside crypto trading.
User control remains central to the design
Unlike fully autonomous trading systems, Kraken’s AI agents do not execute trades without approval. Users can review AI-generated portfolio suggestions, adjust them and confirm before any transaction is placed. The company positions the technology as a decision-support tool rather than an automated trading system. Kraken said the app provides maximum visibility and maximum control with minimum effort.
Crypto exchanges race to embed AI trading tools
Kraken is not alone in this shift. Coinbase and Gemini have already advanced AI-assisted trading and developer tools to make sophisticated features more accessible to retail investors. The move reflects a structural change similar to the rise of mobile trading or algorithmic investing, where access alone does not guarantee confidence or success for everyday users.
Final Thoughts
Kraken’s app overhaul signals a turning point for crypto exchanges moving into broader financial services. By embedding AI agents into core architecture rather than bolting them onto old interfaces, Kraken aims to level the playing field between retail and professional traders. The feature launches soon.
FAQs
No. Every trade recommendation requires user approval before execution. Users review AI-generated portfolio suggestions, adjust them and confirm before any transaction is placed.
The AI learns user financial goals, risk tolerance, funding preferences and financial profile during onboarding. It then builds a draft portfolio based on that information for users to review.
The AI monitors markets continuously around the clock, identifying investment opportunities and recommending trades in real time based on user goals and preferences.
Kraken is expanding beyond crypto trading into payments, banking and lending. The new app reflects this shift toward a broader financial platform.
Disclaimer:
The content shared by Meyka AI PTY LTD is solely for research and informational purposes. Meyka is not a financial advisory service, and the information provided should not be considered investment or trading advice.
About Author

Huzaifa Zahoor
Co FounderHuzaifa Zahoor is the engineer who built Meyka. He has spent years writing Python, training AI models, and building data pipelines specifically for financial markets. His technical articles have reached over 30,000 readers on Medium, so he knows how to make complex things easy to follow. If this article touches on how the tools work, he is the person who actually built them.
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