AI has occupied a large part of the attention on Web3. However, the actual value provided by most AI agents is currently limited - this situation is about to change.

Now many DeFi protocols have incorporated AI agents to provide users with new automated and AI-driven experiences.

This evolution has given rise to a new DeFAI movement (DeFi + AI). However, these discussions often overlook a key participant: perpetual exchange (Perp) DEX. So what will happen when AI agents encounter perpetual trading, and how can we leverage PerpAI?

PerpAI: Potential Use Cases

AI agents are poised to fundamentally change the way we interact with everything, including cryptocurrencies. Here are some new use cases that may emerge from the combination of perpetual futures trading and AI agents.

We have already seen examples of AI agents, such as those from Spectral (https://x.com/@Spectral_Labs), trading on Hyperliquid. But what specific use cases can integrate Perp DEX into their platforms?

1. Major Perp DEX Collaborating with aixbt_terminal

Platforms such as SynFutures, Hyperliquid, Jupiter, or dYdX dominate the perpetual contract trading volume. As the leading perp DEX on Base, SynFutures may have a strategic advantage here, as aixbt_agent is also on Base.

Imagine a "Degen Mode" that leverages aixbt's insights to perform automated trading on SynFutures or other perp DEX. This mode can not only integrate social analytics and news, but also integrate perp native data, such as open interest (OI), trading volume trends, and funding rates.

Example Extension: For instance, imagine a scenario where AI identifies a sudden spike in the funding rate of the BTC perpetual contract due to an increase in long positions. It can then initiate a contrarian short trade to maximize the profitability of the over-leveraged traders.

Access to these features can be granted through dual-token ownership or dual-equity (this is just speculation, as the team is likely to innovate in their own way).

2. AI Agents Managing Liquidation Risk

For the DEX that is the first to adopt this use case, it could easily become a killer feature. By monitoring funding rates, volatility, and collateral health, AI agents can dynamically adjust leverage levels to manage liquidation risk.

Example Extension: Suppose a user's collateral is primarily ETH, and the market experiences a sharp drop in ETH prices. If the margin buffer becomes too thin, the AI agent can dynamically rebalance the collateral to stablecoins to reduce liquidation risk, or even partially close the position.

In more advanced setups, it can use options for hedging, provided the perp platform supports such integrations. This approach can ensure traders can sleep soundly, knowing their positions are protected in real-time.

3. AI Agents as Personal Trading Mentors

Just as post-game analysis in online chess highlights missed opportunities and mistakes, AI agents can provide a similar experience for traders.

Example Extension: Imagine an AI agent generating a comprehensive trade review, detailing areas for improvement, such as "you exited this trade too early; historical data shows that holding for an additional hour would have increased profits by 15%." It can also suggest alternative strategies based on historical success rates, such as "consider using a trailing stop-loss for trend-following trades."

This concept opens up a new revenue stream for experienced traders: allowing AI agents to analyze their trades and understand the factors influencing their entry and exit points. Over time, the AI will become smarter, able to identify common patterns among successful traders, and serve as a mentor for less experienced users.

This service can be offered as a paid feature, with the highest-performing traders receiving a revenue share. Alternatively, it can evolve into an automated, AI-driven trader that learns from the best human traders and mimics their frameworks to generate high-confidence trades.

4. Liquidity AI Collectives

This idea focuses on the other side of trading: liquidity. AI agents can analyze factors such as volatility, market depth, and trading activity to create a "collective intelligence" that dynamically rebalances liquidity across different markets and platforms.

Example Extension: Imagine a scenario where a surge in demand for a specific asset leads to a liquidity crunch in the market. The AI collective can proactively identify this situation and reallocate liquidity from markets with lower demand, stabilizing the spreads and minimizing slippage for traders.

In essence, this means that all DEXs would have a unified liquidity pool, with AI agents directing liquidity to the markets with the highest demand. By strategically allocating resources, this approach can significantly improve capital efficiency and deliver higher-than-average returns for liquidity providers.

Key Figures to Watch

Before these innovations become the new gold standard for perp DEX, who is likely to be among the first to adopt these ideas (or perhaps their own take on AI agents)?

Particularly in the case of AI risk managers, the leaders are most likely to emerge from the existing giants in the perpetual contract space, dominating in terms of trading volume and market capitalization.

I'm particularly bullish on the on-chain DEXs with high demand for AI agents and high adoption rates, such as Jupiter and SynFutures. Of course, we can't overlook the rising star Hyperliquid in the trading markets.

Integrating AI agents into DeFi (especially perpetual futures DEX) represents not just an incremental improvement, but a true paradigm shift. By leveraging AI tools, traders can unlock smarter, safer, and more efficient ways to navigate the markets. At the same time, the early adopters of these innovations will position themselves as pioneers of the DeFAI movement.