Author: Stacy Muur, Crypto KOL

Compiled by: Felix, PANews

You’ve probably noticed this: many DeFi protocols are now incorporating AI agents:

Many DeFi protocols are now beginning to integrate AI Agents:

• Navigating trading trends
• Providing 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 meet Perps? How can we leverage PerpAI?

PerpAI: Potential Use Cases

AI Agents are expected to revolutionize the way we interact with everything, including cryptocurrencies. Here are some potential new use cases that may arise at the intersection of perpetual trading and AI Agents.

We have already seen use cases for AI Agents, such as trading from Spectral on Hyperliquid. But what use cases can perpetual DEXs integrate into their platforms?

1. Large Perpetual DEXs in Collaboration with aixbt

Currently, platforms like SynFutures, Hyperliquid, Jupiter, or dYdX are dominating perpetual contract trading. SynFutures, as a leading perpetual DEX on Base, may have a strategic advantage since aixbt's headquarters is also on Base.

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

Example expansion: Imagine a scenario where AI identifies a sudden spike in funding rates for BTC perpetual contracts due to an increase in long positions. It could initiate a counter-trend short trade, maximizing profitability from over-leveraged traders on the other side.

Access to these features could be granted through dual staking or dual token ownership (just a guess, as the team tends to innovate in their own way).

2. AI Agents Managing Liquidation Risks

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 risks.

Example expansion: Suppose a user's collateral is primarily ETH, and the market experiences a sharp decline in ETH prices. AI Agents could dynamically rebalance the collateral to stablecoins to reduce liquidation risk, or even partially close positions when margin is too low.

In more advanced setups, if a perpetual platform supports such integration, it could use options for hedging. This approach could provide traders with peace of mind, knowing their positions are protected in real-time.

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 could provide traders with a similar experience.

Example expansion: 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 another hour would have increased profits by 15%." They could also suggest alternative strategies based on historical success rates, such as "Consider using a trailing stop in trend-following trades."

This concept opens up new revenue streams for experienced traders: allowing AI Agents to analyze their trades and understand the factors influencing entry and exit price levels. Over time, AI becomes smarter, capable of identifying common patterns among successful traders and providing guidance to less experienced users.

This service could be offered as a paid feature, providing revenue sharing for traders with the highest return on investment. Alternatively, it could evolve into an automated AI-driven trader that learns from the best traders and mimics high-confidence trades based on their 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 form of "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. AI clusters could detect this in advance and reallocate liquidity from lower-demand markets to stabilize spreads and minimize slippage for traders.

In practice, this means all perpetual DEXs would have a unified liquidity pool, with AI Agents directing liquidity to high-demand markets. This approach could significantly enhance capital efficiency and provide LPs with above-average returns through strategic resource allocation.

Key Players to Watch

Before these innovations become the new gold standard for perpetual DEXs, who might be the first teams to realize these ideas?

I am optimistic about on-chain DEXs like Jupiter and SynFutures that have high demand and adoption rates for AI Agents. Of course, we cannot overlook the newly emerging Hyperliquid.

The integration of AI Agents with DeFi, especially perpetual 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 market. Meanwhile, platforms that adopt these innovations early can position themselves as pioneers of the DeFAI movement.

Related reading: AI Agent sector rebounds strongly, a look at 10 emerging AI Agent projects to watch

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