Bitget, the world’s largest Universal Exchange (UEX), has rolled out a new account structure that allows its AI trading agent, GetClaw, to operate independently within a dedicated environment.
The update enables the agent to execute live trades based on natural language instructions, continuously monitor market conditions, and manage positions without manual input.
This development builds on earlier advancements, including the launch of GetClaw as a zero-installation AI assistant and the expansion of Agent Hub, which introduced integrated analytics and data tools.
Together, these steps reflect a broader evolution from providing market access, to delivering insights, and now to enabling direct execution through AI systems.
AI-led trading
The introduction of agent-specific accounts highlights a growing trend in trading, where AI is moving beyond advisory roles into active participation.
By assigning separate environments for automated strategies, Bitget allows users to deploy and manage AI-driven trading with clearer oversight and defined parameters.
According to CEO Gracy Chen, the shift toward AI-led trading is inevitable, with infrastructure now being developed to support large-scale adoption.
“Sooner or later emerging financial markets are going to be filled with AI agents trading on behalf of users. We’re preparing the infrastructure to run this on scale,” said Gracy Chen.
Rather than positioning AI as an external tool, Bitget is embedding it directly into its trading ecosystem. Through Agent Hub, these systems can access live data, perform analysis, and execute trades within a unified framework, reducing reliance on fragmented workflows.
As automation becomes more prevalent, this model reflects the emergence of “agentic trading,” where systems independently interpret market signals and act on them.
Within Bitget’s broader Universal Exchange framework, the addition of agent accounts signals a move toward integrated environments where human and AI-driven trading operate side by side.
Dan Burgin
U.Today Editorial Team
Vladislav Sopov
