A new Messari Pulse report highlights early adoption trends across Bitget’s AI trading infrastructure, describing a four-layer product stack that extends artificial intelligence into analysis, execution, developer tooling, and user engagement. According to the release, Bitget built this AI framework as part of its broader exchange infrastructure, which the company says serves 125 million users worldwide.
The report centers on four products: GetAgent for conversational market analysis, GetClaw for autonomous trade execution, Agent Hub as the infrastructure layer connecting AI systems to exchange functions, and Gracy AI, a strategic guidance interface built around the public market voice of Bitget CEO Gracy Chen. Taken together, these tools are positioned as an attempt to integrate multiple stages of the trading workflow into one platform rather than treating AI as a standalone feature.
User Traction Emerges Across Multiple AI Products
One of the report’s main takeaways is the speed of early user uptake. Based on Bitget data cited by Messari, Gracy AI attracted more than 460,000 users within its first 11 days after launching in February 2026. During that same period, the product reportedly generated more than 2.6 million replies and delivered over 390 million impressions. Those figures suggest that interest in AI-guided trading interfaces is not limited to experimentation, but may be broadening into meaningful user engagement at scale.
The report also points to strong traction for GetAgent, which has surpassed 450,000 registered users since launch. Before broader availability, GetAgent went through an invite-only phase from July to August 2025. During that period, Bitget says the product generated more than 100 million impressions and built a waitlist of over 25,000 users. In practical terms, that sequence indicates market interest developed well before the product reached larger-scale deployment.
For exchange operators and trading platform observers, these adoption numbers matter because they provide a rare public snapshot of how users are engaging with AI-native tools in a live crypto trading environment. While user counts and impressions do not automatically translate into sustained usage or financial outcomes, they do indicate that AI-assisted trading products are gaining visibility and attracting attention beyond a narrow group of early adopters.
Four Layers of AI Trading Infrastructure
Messari’s framing of Bitget’s AI architecture is notable because it breaks the stack into distinct functional layers. GetAgent is positioned as the analytical layer, offering conversational market analysis designed to help users interpret market conditions. GetClaw acts as the execution layer, where AI can move from recommendations into action. Agent Hub serves as the developer and infrastructure layer, enabling outside systems and AI agents to access exchange capabilities. Gracy AI, meanwhile, is built as a user-facing guidance tool shaped around the public market commentary of the company’s CEO.
This structure reflects a broader trend in financial technology: AI products are increasingly being developed not as single chat interfaces, but as modular systems spanning research, strategy, execution, and infrastructure. In that context, Bitget’s stack appears designed to cover the full pathway from market interpretation to order placement, while also giving developers tools to build additional applications on top of exchange functions.
Messari notes that this integrated approach extends AI across four major areas inside the Bitget platform: analysis, execution, infrastructure, and engagement. That positioning is important because many exchanges have so far approached AI more narrowly, often limiting it to support chatbots or basic research tools. Here, the report suggests Bitget is trying to embed AI directly into how trading activity is organized and accessed.
Agent Hub Focuses on Developer Access
The report places special attention on Agent Hub, which launched in February 2026 as the infrastructure layer for connecting AI systems directly to exchange functions. According to Messari, Agent Hub supports MCP Server, Skills, REST APIs, WebSocket APIs, and a command-line interface. The report says Bitget is the only exchange currently offering all four of those access routes simultaneously.
That claim, if sustained, would make Agent Hub a significant piece of infrastructure for developers who want to build AI-driven trading tools that can operate with market data, signals, and execution pathways in one environment. Bitget has also expanded the platform to include five analytical AI Skills and more than 15 integrated data tools. Those tools cover macro analysis, technical signal detection, sentiment monitoring, market intelligence, and news aggregation.
For developers, this kind of toolkit could lower the barrier to building trading assistants, automated workflows, or strategy overlays. For exchanges, it suggests a future in which AI agents may function less like external add-ons and more like native participants within platform ecosystems. Even so, the long-term utility of this model will depend on reliability, security, and whether developers actually build sustained usage on top of the available interfaces.
GetClaw Adds Autonomous Execution With Guardrails
On the execution side, GetClaw is described as an autonomous trading layer designed with constraints aimed at retail risk control. According to the release, trades are executed through dedicated sub-accounts that are isolated from user-held assets. The product also uses sandbox environments and fund limits to define where the agent can operate and how much capital it can deploy.
These details are important because autonomous execution remains one of the more sensitive applications of AI in crypto markets. While AI-generated insights can be useful, allowing software agents to place trades introduces a different category of operational and financial risk. By isolating execution through sub-accounts and limiting the capital range available to the agent, Bitget appears to be trying to reduce the chance that users expose their full balances to automated strategy errors.
At present, GetClaw is live on Telegram, with future expansion planned for Discord, WhatsApp, and in-app deployment. That rollout strategy implies Bitget is meeting users where they already spend time, rather than restricting access to web-based exchange dashboards alone. It also reflects the growing overlap between messaging platforms and crypto trading behavior, particularly for users who follow signals, communities, and market updates in real time.
Bitget’s Strategic Positioning Around AI
In the release, Bitget CEO Gracy Chen said the company wants to provide billions of people with the ability to trade more like Wall Street professionals. She also said AI is becoming part of how modern trading infrastructure is built, arguing that early adoption across Bitget’s AI products shows users increasingly expect analysis, execution, and strategy to be integrated within one platform.
That statement aligns with the broader narrative emerging across digital asset markets: exchanges are no longer competing only on listing breadth, liquidity, or fees. They are also competing on intelligence layers, workflow efficiency, and how effectively they can turn data into decisions for users with different levels of sophistication. In that sense, AI may become a differentiator not just for institutions, but for retail-facing crypto platforms as well.
Bitget also uses the announcement to reinforce its broader market identity. The company describes itself as the world’s largest Universal Exchange (UEX), offering access to more than 2 million crypto tokens as well as 100+ tokenized stocks, ETFs, commodities, FX products, and precious metals such as gold. It says it currently leads in the tokenized TradFi market while operating across 150 regions worldwide. Those claims form part of the backdrop for the AI rollout, presenting the stack as one layer within a wider multi-asset exchange strategy.
Context and Caution
It is worth noting that the source material is a sponsored press release provided by Bitget, and the article states that it was not written by the host news outlet. As with all company-issued adoption figures, the metrics should be read as reported by the firm and cited by Messari in the referenced report. They offer useful visibility into product momentum, but they do not independently verify long-term retention, user profitability, or the commercial durability of the AI stack.
The release also includes a standard risk warning, noting that digital asset prices are volatile and that investors should only allocate funds they can afford to lose. That caution is especially relevant in the context of AI-driven and autonomous trading tools. Even when guardrails are present, automation does not eliminate market risk, execution risk, or the possibility that strategies underperform in changing market conditions.
Still, the report’s central message is clear: Bitget’s AI trading products are moving beyond concept-stage visibility and into measurable usage. With hundreds of thousands of users, multi-layer infrastructure, and a stated push into analysis, execution, and developer tooling, the company is trying to define what a more AI-native crypto exchange could look like. Whether that early momentum turns into a durable competitive edge will depend on adoption quality, product performance, and how users respond as autonomous tools become more deeply embedded in everyday trading workflows.