AI has captured a significant portion of attention in Web3. However, the actual value provided by most AI agents is currently limited --- --- this is about to change.

Now, many DeFi protocols are incorporating AI agents to offer users 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 player: perpetual trading (Perp) DEX. So, what happens when AI agents meet perpetual trading, and how can we leverage PerpAI?

PerpAI: Potential Use Cases

AI agents are expected to fundamentally change the way we interact with everything, including cryptocurrencies. Here are some new use cases that may arise 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 could integrate Perp DEX into their platforms?

1. Large Perp DEX Collaborating with aixbt_terminal

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

Imagine a "Degen Mode" that utilizes insights from aixbt for automated trading on SynFutures or other perp DEXs. This mode could integrate not only social analysis and news but also native perp data, such as open interest (OI), trading volume trends, and funding rates.

Example Expansion: For instance, envision a scenario where, due to an increase in long positions, AI identifies a sudden spike in the funding rate for BTC perpetual contracts. It could initiate a counter-trend short trade to maximize profitability against over-leveraged traders.

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

2. AI Agents Managing Liquidation Risks

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

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

In more advanced setups, it could use options for hedging, provided the perp platform supports such integrations. This approach ensures traders can rest easy knowing their positions are safeguarded in real-time.

3. AI Agents as Personal Trading Mentors

Just like post-game analyses in online chess highlight missed opportunities and mistakes, AI agents can provide a similar experience for traders.

Example Expansion: Imagine an AI agent generating a comprehensive post-trade report detailing areas for improvement, such as "You exited this trade too early; historical data shows that holding for another hour would have increased profits by 15%." It could also suggest alternative strategies based on historical success rates, like "Consider using a trailing stop for trend trades."

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

This service could be offered as a paid feature, providing revenue sharing for the highest ROI traders. Alternatively, it could evolve into an automated AI-driven trader that learns from the best humans and mimics high-confidence trades based on its framework.

4. Liquidity AI Collective

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 various markets and platforms.

Example Expansion: Imagine a scenario where a liquidity crunch occurs due to increased demand for a specific asset. The AI collective could detect this in advance and reallocate liquidity from markets with lower demand to stabilize spreads and minimize slippage for traders.

In practice, this means all DEXs would have a unified liquidity pool, with AI agents directing liquidity to markets with high demand. By strategically allocating resources, this approach could significantly enhance capital efficiency and provide liquidity providers with above-average returns.

Key Players to Watch

Before these innovations become the new gold standard for perp DEXs, who are the teams likely to be the first adopters of these ideas (or perhaps their own views on AI agents)?

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

I personally have high hopes for on-chain DEXs like Jupiter and SynFutures, which have high demand and adoption rates for AI agents. Of course, we cannot overlook the legendary Hyperliquid, which is rising in the trading market.

Integrating AI agents into DeFi (especially perpetual futures DEXs) is not just a gradual improvement; it represents a true paradigm shift. By leveraging AI tools, traders can unlock smarter, safer, and more efficient ways to navigate the markets. Meanwhile, platforms that adopt these innovations early will position themselves as pioneers of the DeFAI movement.