As AI Agent hype continues to rise, one clear change stands out: more and more developers are shifting from "using AI" to "building AI Agents." At first glance, the difference seems only to be in the name. In reality, the development logic is very different.
Traditional AI applications typically center around building a single model: after you provide input information, it generates content. Agent applications, however, must go further by connecting to external services. That way, the model can fetch real-time information, call tools, and complete specific tasks.
This means the problems developers face start shifting from the model itself to the underlying infrastructure. Whether an Agent is truly useful depends, to a large extent, on what capabilities it can access—and whether those capabilities can be invoked reliably and consistently.
This is especially true in the digital asset space. Services like trading, wallets, on-chain interactions, market data, and payments are inherently highly specialized. If developers have to build the underlying connections for every single Agent from scratch, AI Agent innovation speed is easily constrained by basic infrastructure work.
Where Agent Applications Really Get Hard
Building an AI application that can chat isn’t difficult. But building an Agent that can truly complete tasks significantly increases complexity. The model is only part of the picture. Developers also need to solve tool integration, identity verification, task execution, error handling, data retrieval, and the orchestration of different services. As the tasks an Agent must perform become more complex, these surrounding efforts often consume a large share of development time.
More importantly, Agent applications have clear scenario differences. An Agent dedicated to market research needs strong information and data capabilities. A trading Agent needs trading interfaces. An asset management Agent may need both wallet and account-related capabilities at the same time. If developers build from zero, they’ll repeatedly solve a large number of similar problems.
As a result, Agent development has begun to evolve into a new division of labor: the model and application layer drive innovation, while infrastructure provides common capabilities.
This division looks a lot like past software development. Developers don’t redesign databases, network protocols, and basic compute capabilities for every app. Instead, they build their products on top of mature infrastructure. AI Agents are gradually moving into a similar phase.
Why Digital Asset Development Is More Complex Than "Ordinary" Agent Development
The special nature of digital asset Agents is that they connect to a highly open and continuously evolving ecosystem. Traditional Agents might only need to search the web, process documents, or call a specific SaaS service. But digital asset Agents may simultaneously deal with centralized exchanges, decentralized exchange protocols, wallets, on-chain data, and payment services.
These capabilities don’t share the same technical logic. Centralized exchanges involve account and trading interfaces. DEXs require on-chain protocol interactions. Wallets relate to asset management. News and data require continuous access to external information. Each of these can become an Agent’s tool, but the underlying integration logic is not simple.
If developers build every connection for an Agent independently, the initial work might be just dozens of lines of code or a few interfaces. But as features expand, system maintenance costs can rise quickly. If any underlying service changes, developers may need to adjust their Agent again.
That’s why digital asset Agents particularly need a capabilities layer. Its role isn’t to complete the entire Agent for developers. Instead, it abstracts complex digital asset capabilities further, so developers can build directly around application requirements.
How Much Repeated Buildwork Can One Set of Core Capabilities Reduce?
The most direct value of infrastructure is turning repetitive labor into reusable capabilities. Imagine multiple developers all want to build digital asset Agents. They likely all need market information, trading, wallet, or on-chain interaction capabilities. If each person develops everything separately, those tasks get duplicated many times.
Once these capabilities are encapsulated, developers can build their applications directly on top of existing modules. One developer can focus on a market research Agent. Another developer can build an asset management Agent. Still others can design products around trading execution, data analysis, or other scenarios.
This also changes what "innovation units" look like for Agents. In the past, developers needed to build products starting from "core capabilities." Going forward, more developers may start from "task scenarios." Core capabilities are provided by infrastructure, while the application layer decides how to combine them.
This shift is crucial for the entire Agent ecosystem. Only when core capabilities mature enough can developers quickly try more vertical scenarios. Only then can the number and variety of Agent applications truly expand.
What Gate for AI Agent Provides Developers
Gate for AI Agent’s positioning is to provide a foundation of digital asset capabilities for this development model. It connects multiple directions—CEX, DEX, Wallet, News, Info, and Pay—so when developers build AI Agents, they can access the appropriate digital asset capabilities based on different application needs.
For example, a market-analysis-focused Agent can primarily use information and data capabilities. A trading-focused Agent can further integrate CEX or DEX-related capabilities. If an application needs to handle asset management, it can use Wallet-related capabilities. Different applications don’t need to fully replicate the same architecture; instead, they choose the required modules based on the actual tasks.
This approach doesn’t just reduce code volume. More importantly, it lowers the barrier for teams to enter the digital asset Agent space.
For teams without deep digital asset development experience, if every underlying capability must be researched in-house, the project can easily spend a lot of time before the application truly goes live. With infrastructure integration, development teams can move faster and focus attention on product logic, user needs, and specific scenarios.
At the same time, Gate for AI Agent also provides multiple capability integration paths through Gate Skills, CLI, MCP, API, and more. Developers can choose based on their development environment and application needs.
This means Agent infrastructure is no longer just a collection of interfaces. It’s gradually becoming a capability foundation developers can rely on when building digital asset AI applications.
How Skills Make Agent Development More Modular
If infrastructure answers "where these capabilities come from," then Skills answers "how these capabilities can be used more conveniently by Agents."
Traditional software development often requires developers to rewrite an entire function into application logic. Skills, meanwhile, can further package specific capabilities into modules that Agents can use.
This model is especially suitable for AI Agents, because Agents themselves need to dynamically select tools based on tasks. A mature Skill doesn’t require developers to redesign the entire calling flow every time. Instead, it can serve as a component within an Agent’s capability system.
The development of Gate Skills Hub also reflects this modular trend. Its capabilities span different directions, including market analysis, trade execution, arbitrage opportunity identification, and risk analysis. It also offers integration methods tailored to different development environments.
For developers, this means building an Agent can move further from "writing a complete application" to "selecting capability modules + designing task logic."
Of course, each Agent’s core value still comes from the application itself. Skills won’t automatically decide what problem an Agent should solve. But it can reduce the cost of building underlying capabilities, making it easier for developers to innovate around scenarios.
As more and more capabilities become modular, Agent development can form something like a component system in a software ecosystem. Developers no longer need to start from zero; they can keep combining, modifying, and extending existing capabilities.
From Single Applications to Developer Ecosystems: Agent Infrastructure Is Expanding
As core capabilities become standardized, not only individual developers’ efficiency changes—but the way the ecosystem innovates can change too. If every Agent needs to build underlying capabilities independently, it’s hard for developers to form effective reuse. The capabilities accumulated by a project often serve only that project’s own application.
But when capabilities can be used by more Agents in a modular way, a clearer layering emerges between infrastructure and applications. The bottom layer provides common capabilities, while developers on top create applications around different needs.
This structure supports a positive feedback loop. More core capabilities attract more developers. More developers generate new application scenarios. New scenarios then further enrich the underlying capabilities.
For the digital asset industry, this kind of ecosystem evolution deserves special attention. In the past, Web3 application innovation mainly focused on trading platforms, wallets, protocols, and data services. As AI Agents enter this space, new application layers may be built on top of those core capabilities.
You can also understand Gate for AI Agent’s role within this layer. It connects digital asset core capabilities, and developers can build Agents tailored to different needs on top of them.
Ultimately, users may only see a specific AI Agent. But behind the scenes, it may connect an entire suite of trading, wallet, news, data, and payment capabilities. The more mature the infrastructure becomes, the easier it is for more sub-directions to appear in the application layer.
The Next Stage of AI Agent Development: Not Just Model Competition
At a certain stage of AI Agent development, model capabilities still matter—but they’re no longer everything. If an Agent can understand complex problems but can’t access external data and digital asset services, it’s still closer to an advanced chat tool. Only when model capabilities connect to real-world services can an Agent truly take on more tasks.
So, in the future, the Agent ecosystem may gradually form several layers that work together: the model handles understanding and reasoning; infrastructure connects external capabilities; developers build specific applications; and users ultimately complete their tasks through the Agent.
Among these, infrastructure is a layer that’s easy to overlook. But it determines whether model capabilities can genuinely extend into real-world scenarios. Gate for AI Agent connects digital asset capabilities such as CEX, DEX, Wallet, News, Info, and Pay. In essence, it fills the missing connection layer between models and the digital asset world. With Gate Skills, CLI, MCP, API, and more, developers can further bring these capabilities into their own Agent applications.
From this perspective, AI Agent development isn’t just a single round of model upgrades. It may also trigger a new wave of building application infrastructure. What’s truly worth watching in the future isn’t only which models get stronger—but also which core capabilities can be used by more Agents, and what kinds of new applications developers can create on top of them.
Once digital asset capabilities become modular, standardized, and opened up to Agents, the way developers build AI applications will change too. Scenarios that once required heavy underlying development may only require reorganizing existing capabilities around specific needs.
Perhaps this is the deeper meaning of Agent Infrastructure: not to replace innovation for developers, but to lower the cost of repeated buildwork—so more developers can invest their time in places that truly deserve innovation.
FAQ
Why Does AI Agent Development Need Infrastructure?
AI Agents need more than models. They also need access to external data and services. Infrastructure can provide standardized capability connections and reduce the work developers must repeat when building underlying functions.
What Are the Key Challenges in Developing Digital Asset Agents?
Digital asset Agents may involve different capabilities at the same time, such as CEX, DEX, Wallet, market data, news, and payments. Since the technical logic of each service differs, the cost of underlying integration and maintenance is higher.
What Does Gate for AI Agent Offer Developers?
Gate for AI Agent connects digital asset capabilities such as CEX, DEX, Wallet, News, Info, and Pay. It also provides capability integration paths through Gate Skills, CLI, MCP, API, and more.
How Does Gate Skills Help AI Agent Development?
Gate Skills can further modularize some digital asset-related capabilities, allowing developers to select and combine only the capabilities they need for specific applications, thereby reducing repeated development.
Will AI Agent Infrastructure Replace Developers?
Infrastructure mainly solves the problem of connecting underlying capabilities, and it won’t replace developers’ application design. Developers still need to decide which users their Agent serves, what problems it solves, and how to organize the specific task logic.
