Bitget Integrates Crypto and Traditional Finance with GetAgent 2.0, Enhancing Market Analysis and User Insights
September 23, 2026
GetAgent’s capabilities are integrated with Bitget’s UEX development to unify crypto and traditional financial instruments on a single platform, with ongoing growth in assets and functions.
The rollout is currently available to Bitget users, with plans to expand asset classes and market themes across the platform.
GetAgent 2.0 is available to Bitget users and will continue expanding supported assets and capabilities.
Users can schedule reports and alerts for specific market events, transforming GetAgent from a query tool into a proactive research assistant.
The system organizes market data, events, and fundamentals into analyses that cover participation conditions, key levels, and risk considerations for assets.
GetAgent 2.0 supports follow-up research based on user instructions and areas of interest, extending conversations over time.
The upgrade adds risk-factor analysis for crypto exchanges to provide more context when monitoring events by asset class.
The upgrade adds deeper market analysis and scheduled tasks, enabling ongoing research beyond single queries.
GetAgent 2.0 analyzes single assets and inter-market relationships, allowing study of how Bitcoin movements relate to equities, commodities, and broader economic trends, and helps identify key price levels and potential market scenarios.
GetAgent 2.0 enables examination of relationships between assets, such as how gold, equities, or macro conditions may relate to crypto.
Bitget CEO Gracy Chen highlighted integrating multiple markets to help users see connections and maintain awareness across asset classes.
Chen notes GetAgent assists cross-market connections (e.g., Bitcoin, gold, Nvidia, oil) and ongoing awareness of developments.
Summary based on 2 sources
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Sources

CoinGape • Sep 23, 2026
Bitget Expands GetAgent 2.0 With AI Research Across Crypto, Stocks, Gold and Oil
• Sep 23, 2026
Bitget expands GetAgent 2.0 to multi-asset AI research