Source: Stacy Muur X account
Author: Stacy Muur, Crypto KOL
Compiled by: Felix, PANews
You must have noticed this: many DeFi protocols are now incorporating AI agents:
Many DeFi protocols are now starting to integrate AI Agents:
- Harness trading trends
- 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 factor: perpetual DEXs. So, what happens when AI Agents encounter Perps? How can we leverage PerpAI?
PerpAI: Potential Use Cases
AI Agents hold the promise of fundamentally changing the way we interact with everything, including cryptocurrencies. Here are some potential new use cases that may emerge when perpetual trading and AI Agents intersect.
We've already seen use cases for AI Agents, such as trading on Hyperliquid from Spectral. But what use cases can perpetual DEXs integrate into their platforms?
1. Large Perpetual DEX Collaborating with aixbt
Currently, platforms like SynFutures, Hyperliquid, Jupiter, or dYdX are leading the perpetual contract trading space. SynFutures, as the leading perpetual DEX on Base, may have a strategic advantage, as aixbt's stronghold is also on Base.
Imagine a "Degen mode" that leverages aixbt's insights to automate trading on SynFutures or another perpetual DEX. This mode could not only integrate social analytics and news, but also native data such as open interest (OI), trading volume trends, and funding rates.
Example extension: Imagine a scenario where the AI identifies a sudden spike in the funding rate of the BTC perpetual contract due to an increase in long positions. It could then trigger a counter-short trade to maximize profitability from the over-leveraged traders on the other side.
Access to these features may be granted through dual staking or dual token ownership (just speculation, as the teams tend to innovate in their own way).
2. AI Agents Managing Liquidation Risk
For early-adopting DEXs, this use case could become a killer feature. By monitoring funding rates, volatility, and collateral health, AI Agents can automatically 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. AI Agents could dynamically rebalance the collateral to stablecoins to reduce liquidation risk, or even partially close positions if the margin is too low.
In more advanced setups, if the perpetual platform supports such integration, it could use options for hedging. This approach can give traders peace of mind, knowing their positions are actively protected.
3. AI Agents as Personal Trading Mentors
If you've ever played online chess, you may have encountered post-game analysis, highlighting missed opportunities and mistakes. AI Agents can provide traders with a similar experience.
Example extension: Imagine a scenario where AI Agents generate a comprehensive post-trade report, 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 could also suggest alternative strategies based on historical success rates, such as "consider using a trailing stop-loss in 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 prices. As the AI becomes more intelligent over time, it can identify common patterns of successful traders and provide guidance to less experienced users.
This service could be offered as a paid feature, providing revenue-sharing for the traders with the highest investment returns. Alternatively, it could evolve into an automated, AI-driven trader that learns from the best traders and mimics their high-confidence trading frameworks.
4. Liquidity AI Clusters
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, due to increased demand for a specific asset, the market experiences a liquidity crunch. The AI cluster could detect this early and reallocate liquidity from lower-demand markets to stabilize the spreads and minimize slippage for traders.
In practice, this would mean that all perpetual DEXs have a unified liquidity pool, with AI Agents directing liquidity to high-demand markets. This approach can significantly improve capital efficiency by strategically allocating resources and deliver higher-than-average returns for LPs.
Key Participants to Watch
Before these innovations become the new gold standard for perpetual DEXs, which teams are likely to be the first to realize these ideas?
I'm personally bullish on the on-chain DEXs with high demand and adoption for AI Agents, such as Jupiter and SynFutures. Of course, the emerging Hyperliquid can't be overlooked either.
The integration of AI Agents with DeFi, especially perpetual DEXs, 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, early adopters of these innovations can position themselves as pioneers of the DeFAI movement.