Over the past year, AI Agents have remained one of the hottest topics in the tech industry. However, in recent months, a new shift has started to emerge.
Previously, the industry focused on whether "Agents can get things done." Now, more and more companies are asking, "Can Agents perform tasks reliably, continuously, and at a low cost?" Whether it’s major AI companies rolling out new Agent capabilities or a growing number of businesses integrating AI Agents into their R&D, office, and business workflows, it’s clear that Agents are moving into real-world applications rather than serving as experimental showcases of technical prowess. Recently, several tech firms have publicly stated that software development, business operations, and even knowledge work will increasingly involve AI Agents. Discussions around Agent costs, open ecosystems, and long-term operational capabilities have also become much more prevalent.
This shift holds significant implications for the digital asset industry as well.
Compared to many traditional sectors, the digital asset market is inherently digital, real-time, and open. Market data, on-chain information, and trading interfaces all provide a rich operational environment for AI Agents. As the AI industry enters the Agent era, the digital asset market is quickly becoming one of the most representative real-world application scenarios.
AI Agents Are Moving from Experimentation to Production
Historically, the development of large models has focused on enhancing capabilities. Models have become better at understanding and reasoning, enabling AI to tackle increasingly complex tasks. But as model capabilities mature, the industry has realized that having a powerful model alone doesn’t solve real business challenges.
What truly matters is whether AI can operate over the long term. Recent research—including studies from OpenAI—shows that more companies are using AI Agents to complete long-duration, multi-step tasks, rather than just one-off Q&A or content generation. Users are now relying on Agents to manage projects, analyze data, and even execute complex workflows over time. This marks a shift in AI’s role from a "tool" to a "work system."
As a result, the criteria for evaluating an AI Agent will no longer be limited to accuracy. Instead, the focus will be on whether it can consistently complete tasks, reliably call external resources, and integrate seamlessly with real business processes.
In the digital asset market, this trend is especially pronounced. The market generates new prices, on-chain data, project updates, and capital movements every day. If AI can’t continuously access this information, it won’t be able to support users with long-term research and strategy management.
Cost and Efficiency: The New Drivers for AI Agent Adoption
Beyond capability, cost is becoming a key factor in the widespread adoption of AI Agents. Recently, several industry organizations have noted that more companies are reassessing the costs of deploying AI and actively exploring open models and more efficient Agent architectures. Their goal is to lower long-term operational costs while maintaining high execution efficiency. Industry consensus is forming around the idea that future competition among AI Agents will focus not only on model performance but also on overall operational costs and resource efficiency.
This is just as critical for the digital asset industry. A truly valuable AI Agent can’t just run for a few minutes—it needs to monitor markets, analyze data, organize information, and continually update research findings over the long term. If every analysis consumes excessive resources, AI Agents won’t become tools users rely on daily. Therefore, platforms must do more than just provide AI capabilities; they also need to continuously optimize their underlying architecture so Agents can perform ongoing tasks more reliably and efficiently.
That’s why more platforms are now paying attention to Agent infrastructure, not just the models themselves.
How Gate for AI Agent Connects to the Real Digital Asset Market
The true value of an AI Agent largely depends on its access to the real market.
If an AI can understand questions but can’t access trading capabilities, on-chain data, or market information, much of its analysis remains theoretical.
Gate for AI Agent is designed around capability integration. The platform has already combined centralized trading, on-chain trading, wallet interactions, real-time news, and on-chain data modules. This enables AI Agents to work continuously toward user goals, rather than just providing one-off analyses.
For example, when a user wants to monitor a trending sector, AI can automatically track the development of relevant projects, analyze market transactions, follow on-chain capital flows, and incorporate industry news to produce dynamic assessments—instead of waiting for the user to initiate new queries each day.
This approach shifts repetitive research tasks from humans to AI for ongoing maintenance. Users receive not just a one-time analysis, but a constantly updated information system.
For developers, unified capability interfaces make it easier to build Agents without repeatedly integrating trading, wallet, and data functionalities from scratch.
Why Skills Hub Is a Key Component of the AI Agent Ecosystem
If Gate for AI Agent is the operating platform for the ecosystem, then Skills Hub serves as a vital source for continuously expanding AI capabilities.
The recently upgraded Gate Skills Hub now aggregates over 10,000 AI Skills, covering market analysis, strategy research, risk management, trading execution, and more. This means AI Agents are no longer confined to fixed functions—they can flexibly combine various capabilities to tackle different tasks.
What sets this model apart from traditional software is its ability to grow continuously.
As more developers contribute to the ecosystem, new Skills are regularly added to the platform, expanding the range of tasks Agents can handle. An Agent that initially only organized information can later add on-chain analysis, strategy support, or even automated execution—without the need for a complete redesign.
This ongoing evolution is a defining feature that distinguishes the AI Agent ecosystem from traditional software.
The Next Phase for AI Agents: Ecosystem Competition
Many believe that future AI competition will center on models. In reality, industry perspectives increasingly suggest that the gap between models is narrowing, and the real differentiator will likely be the ecosystem. In the future, a top-tier AI Agent will need access to rich data sources, stable capability interfaces, continuously updated Skills, and a secure, reliable execution environment.
The same holds true for the digital asset industry. Future platform competition won’t just be about the number of supported tokens or products, but about who can provide a more complete operating environment for AI Agents—enabling AI to truly participate in market research, asset management, and collaborative trading.
Gate for AI Agent is actively exploring this direction. It’s not just about adding an AI feature; it’s about building a comprehensive collaboration system that connects trading, data, and AI capabilities. As AI Agents become increasingly important participants in the digital asset market, the value of this ecosystem will become even more apparent.
FAQs
Why has interest in AI Agents surged recently?
As more companies deploy AI Agents in real business scenarios, the industry’s focus has shifted from model capabilities to long-term operational stability, cost efficiency, and ecosystem collaboration.
What is the positioning of Gate for AI Agent?
Gate for AI Agent connects trading, on-chain, wallet, news, and data capabilities to provide AI Agents with a real, usable operating environment for digital assets.
What role does Skills Hub play for AI Agents?
Skills Hub has aggregated over 10,000 AI Skills, enabling Agents to quickly acquire professional capabilities in market analysis, strategy research, risk management, and more—with ongoing expansion supported.
Why is the digital asset industry well-suited for AI Agents?
The digital asset market offers open data interfaces, 24/7 trading environments, and highly digital infrastructure, making it ideal for AI Agents to operate continuously and collaborate over the long term.
How will AI Agents impact future platform competition?
Future competition will extend beyond trading products and liquidity to include AI infrastructure, capability ecosystems, and Agent collaboration environments. A platform’s ability to support AI will become a new competitive differentiator.
