#Gate广场AI测评官 When AI Stops Just Watching the Market: Crypto is Entering the Agent Era!

For the past two years, AI has been hot in the crypto space—so hot that new concepts, projects, and narratives keep emerging almost constantly. The market initially focused on "AI concept coins," "AI tracks," and "AI-empowered protocols," which was very exciting. However, products that actually translate into trading have been relatively scarce.

Most tools mainly do things like watching price movements, searching for information, summarizing content, and offering suggestions. But users ultimately still have to switch between pages, check data, connect wallets, find liquidity, and place orders manually. For people who stare at charts daily, chase hot topics, and trade on-chain constantly, this experience isn't necessarily bad, but it's not particularly high-efficiency either.

Recently, AI Agent projects like OpenClaw have suddenly gone viral, pulling the industry's attention back to a more practical question: Can AI evolve from "watching the market with you" to "helping you enter the market"?

This matters especially in crypto because the market is inherently high-frequency, fragmented, and operates 24/7. Information is abundant, changes happen fast, tools are plentiful, and entry points are scattered. The places where people most often lose aren't usually due to poor analysis, but rather due to speed, energy, and process efficiency.

The reason AI Agents are being optimistic about is precisely this: they're starting to have the ability to connect previously scattered actions together, saving users enormous amounts of time switching back and forth.

What nerve did the overnight trending OpenClaw really strike?

OpenClaw's explosive popularity isn't just because it's an AI project—it's because it demonstrated an ability more like being a "digital worker." Traditional AI tools mostly stay at a Q&A level: you ask, it answers; you ask again, it supplements. Agent-type projects like OpenClaw have much larger imagination space because they can understand tasks, invoke tools, and execute actions, stringing together a series of steps into a complete workflow.

In the crypto market, this ability is very vivid: it can first grab market data, then check on-chain data, then read market news and sentiment, next assess risks, find pathways, connect wallets, and finally execute trades. For crypto users, this change in experience is very intuitive.

Previously, you had to have several tools open simultaneously, monitoring exchanges, checking on-chain data, scrolling through quick updates, managing wallets, and judging the sequence yourself. The significance of Agents is that they have the opportunity to bundle these actions together—users only need to provide targets and preferences. Precisely because of this, AI Agents are starting to expand from developer circles and quantitative trading circles into larger groups of trading users. The market is beginning to realize that the next phase's focus might no longer be just "whether AI can talk," but rather "whether AI can do the work for you."

AI Enters en Masse, but Everyone's Actually Doing Different Things

Recently, virtually all major exchanges are making moves toward AI. However, if you break down the product structures, you'll find their approaches aren't entirely the same.

Some platforms lean more toward on-chain systems, placing AI mainly on the DEX trading, wallet interaction, and on-chain data analysis side. For users who frequently participate in on-chain trading, these systems can integrate many previously scattered steps.

Some platforms use AI as an information-layer tool, where AI acts more as an "information filter," helping users see market changes faster, but specific trading actions remain user-performed. This approach is quite direct in improving information acquisition efficiency and relatively easy for average users to accept.

Another path attempts to make trading capabilities themselves callable interfaces for AI, allowing AI to extend beyond the information layer and participate in the trading process. For example, Gate for AI is attempting to integrate exchanges, wallets, on-chain data, and news systems into the same interface architecture, enabling AI to simultaneously access market data, accounts, trading, and on-chain data functions.

Behind this differentiation is actually a reflection of different development philosophies: some platforms prioritize strengthening on-chain interaction capability, some emphasize information analysis capability, and some are beginning to try modularizing the trading process itself, allowing AI to participate at different stages.

The frequently mentioned MCP and Skills can actually be understood as a type of technical structure. MCP is more like a unified interface layer, allowing capabilities like market data, accounts, trading, and on-chain data to be called by AI; Skills are similar to pre-combined functional modules, bundling steps like data analysis, risk assessment, and market judgment together.

As a simple example, if AI can only see price changes, it's hard to judge the market situation; but if it can simultaneously read news, on-chain capital flows, funding rates, order book data, and other information, combined with risk model analysis, it can form a much more complete assessment. The role of Skills is to pre-organize these capabilities, allowing AI to complete an entire analysis workflow faster.

For average users, the changes brought by this kind of design are quite intuitive: fewer tool switches, higher degree of information integration. For more professional traders, such systems could potentially expand in the future to more complex scenarios like arbitrage scanning, risk alerts, and position management.

AI's Next Competition is About Who's More Like a Live Trading Tool

As more and more platforms release AI-related products, the industry's focus is gradually shifting. Early discussions concentrated more on "whether you have AI functionality," but now the market cares more about whether AI can truly participate in the trading process. For trading users, the most direct changes often come from very specific questions:

Can tool switching be reduced? Can hot-spot trading paths be found faster? Can market data, on-chain data, and trading operations all be handled in the same interface? These questions essentially all point in the same direction—can trading processes be organized more efficiently?

One trend that's becoming visible is that AI is gradually transitioning from being an "information auxiliary tool" to being a "process participant." In the past, AI was more often used for market analysis, information summarization, or market sentiment observation, while in new product designs, AI is starting to connect more stages, such as data analysis, risk assessment, trade execution, and even strategy management.

Of course, this stage is still in relatively early development. When it comes to real trading, safety, permission control, risk management systems, and system stability all become important challenges. Many functions currently still require user confirmation or human involvement.

But the overall direction is already quite clear: the value of AI in crypto markets is gradually shifting from "helping understand the market" to "helping execute operations." In the future, differences between exchanges might be more reflected in who can connect information, analysis, and execution stages more smoothly.