GetClaw Integration Advances AI-Driven Trading, Bringing Agentic Execution To Bitget’s Platform

Cryptocurrency exchange Bitget announced that it has launched a new account structure enabling its AI trading agent, GetClaw, to conduct trades autonomously within a dedicated account. This development represents a significant step in AI-driven trading, as the agent can carry out real trades based on natural language instructions, continuously monitor markets, and manage positions in real time without manual oversight.

The move follows the prior introduction of GetClaw, a zero-installation AI agent designed to act as a persistent trading partner, and the expansion of Agent Hub, which incorporated analytical AI Skills and integrated data tools linking market analysis directly with execution. These innovations collectively illustrate a progression from providing access, to intelligence, and now to autonomous execution.

The creation of agent accounts signals an evolution in AI applications within trading. Early systems primarily assisted users with analysis or recommendations, whereas modern agents are capable of continuous market observation and action based on predefined strategies. Assigning dedicated accounts to AI agents allows direct participation under live market conditions.

“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, CEO at Bitget in a written statement.

Using dedicated sub-accounts ensures clear separation between user-controlled assets and agent-driven activity, supporting the deployment of strategies with enhanced transparency and oversight. Users can define strategies in straightforward terms, while GetClaw executes, monitors, and adjusts positions within set parameters.

Bitget Integrates AI Directly Into Trading Infrastructure, Advancing Agentic Trading And Real-Time Execution

This strategy reflects a broader architectural trend. Rather than treating AI as an external feature, Bitget integrates it directly into the trading environment, allowing human users and automated agents to operate on the same infrastructure. Through Agent Hub, AI agents gain access to real-time data, analytical tools, and execution capabilities without fragmented workflows.

As AI participation increases, trading environments are evolving to accommodate both human and machine activity. This shift is contributing to what is described as agentic trading, where systems transition from supporting decision-making to actively participating in markets.

Within Bitget’s Universal Exchange framework, which unifies crypto assets and tokenized traditional instruments in a single account, the introduction of agent accounts extends platform functionality beyond manual trading. As automation becomes more embedded across markets, trading systems are increasingly structured to integrate analysis and execution in real time.

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