Bitget has officially launched GetAgent Playbook, a new strategy workflow layer within its GetAgent platform that marks a significant shift in how artificial intelligence is applied to trading. Rather than relying on conversational prompts, the system introduces structured, reusable playbooks that investors can select, configure, and deploy.
The launch also represents the first user-facing implementation of Agent Harness, Bitget’s internal framework for organizing AI reasoning, execution, and risk management into coherent trading workflows. This technical layer coordinates market analysis, execution logic, and risk controls while enforcing boundaries around position sizing and anomaly detection.
Earlier this year, Bitget reported that more than 1 million users had completed AI-powered trades across tools such as GetAgent and GetClaw, generating cumulative trading volume exceeding $1.2 billion. The new Playbook feature aims to build on that momentum by reducing the complexity of configuring AI prompts for trading strategies.
“AI trading is evolving from Q&As into workflows, and half the complexity of using AI in trading workflows is configuring the prompt,” said Gracy Chen, CEO of Bitget. “With GetAgent Playbook, users can simply pick and choose from a library of ready strategies to plug and play, turning trading ideas into something users can run, adapt, and build on easily.”
Users retain full control throughout the process. Playbooks can be browsed, previewed, configured, subscribed to, launched, and monitored within user-authorized, isolated sub-accounts. The system is designed for transparency, allowing users to review strategy logic, market fit, and risk settings before activation. Playbook will initially be available to GetAgent Plus and Pro users.
Under the hood, Agent Harness coordinates multiple AI models rather than relying on a single one. It enforces execution paths, monitors for anomalies, and logs every action for auditability. This structured approach is intended to move AI trading beyond basic market summarization and question-answering toward more sophisticated, automated strategy execution.
Within Bitget’s Universal Exchange model, GetAgent Playbook extends AI from market interpretation into strategy infrastructure. The move supports Bitget’s broader vision of an Agent-Native Exchange, where intelligent systems become integral to how markets are accessed and operated. Since launching GetAgent, GetClaw, and Agent Hub, Bitget has expanded its AI ecosystem across traders, developers, and autonomous systems.
This development comes amid broader industry trends where AI assistants are increasingly used for market analysis and trade execution. However, Bitget’s focus on workflow orchestration rather than larger models alone suggests a strategic bet on structured automation over raw conversational capability.
For context, other platforms are also expanding their AI offerings. For example, BingX recently launched a $1M stock trading carnival to broaden multi-asset access, while BulkQuant unveiled a 2026 AI crypto trading bot guide comparing platforms for investors. Bitget’s move positions it as a leader in structured AI trading workflows.
This article is for informational purposes only and does not constitute financial advice.
