Explore PerpAI: What are the potential use cases for Perps + AI?

Original source: Stacy Muur X account

Author: Stacy Muur , crypto KOL

Compiled by: Felix, PANews

You must have noticed this: many DeFi protocols now incorporate AI agents:

Many DeFi protocols are now beginning to integrate AI Agents:

• Riding the Trading Trend
• Provide users with new automation and AI-driven experiences

This evolution gave birth to the new DeFAI movement (DeFi + AI). However, these discussions often overlook a key factor: sustainable DEX. So, what happens when AI Agents meet Perps? How to use PerpAI?

PerpAI: potential use cases

AI Agents promise to revolutionize the way you interact with everything, including cryptocurrencies. Here are some potential new use cases that could emerge when perpetual trading and AI Agents intersect.

We are already seeing use cases for AI Agents, such as trading on Hyperliquid from Spectral. But what use cases can a sustainable DEX integrate into its platform?

1. Large-scale sustainable DEX in cooperation with aixbt

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

Imagine a "Degen mode" using aixbt's insights to automate trading on SynFutures or another perpetual DEX. This model can integrate not only 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 funding rates for BTC perpetual contracts due to an increase in long positions. It can initiate contrarian short trades, maximizing profitability from over-leveraged counterparty traders.

Access to these features may be granted via double staking or dual token ownership (just speculation, as the team tends to innovate in its own way).

2. AI Agents manage liquidation risk

This use case will be a killer feature for first-to-adopt DEXs. By monitoring funding rates, volatility, and collateral health, AI Agents can automatically adjust leverage levels to manage liquidation risk.

Example extension: Assume that a user's collateral is primarily ETH and the market experiences a sharp drop in the price of ETH. AI Agents can dynamically rebalance collateral into stablecoins to reduce liquidation risk or even partially liquidate positions when margin is too low.

In a more advanced setup, if a perpetual platform supports this integration, it can be hedged using options. This approach gives traders peace of mind knowing that their positions are protected in real time.

3. AI Agents as personal trading mentors

If you've ever played online chess, you've probably come across post-match analysis, which highlights 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 prematurely; historical data shows that holding on for another hour would have increased profits." 15%". It can also suggest alternative strategies based on historical success rates, such as "Consider using trailing stops 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 that influence entry and exit price levels. Over time, the AI ​​becomes smarter, able to identify common patterns among successful traders and provide guidance to less experienced users.

This service is available as a paid feature, offering a revenue share to traders with the highest ROI. Or it can evolve into an automated AI-driven trader that learns from the best traders and emulates high-confidence trades based on their framework.

4. Liquidity AI cluster

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 "swarm intelligence" that dynamically rebalances liquidity across markets and platforms.

Example extension: Imagine a scenario where a liquidity crunch occurs in the market due to increased demand for a specific asset. AI clusters can spot this in advance and reallocate liquidity from markets with lower demand to stabilize spreads and minimize slippage for traders.

In practice, this means 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 and generate above-average returns for LPs by strategically allocating resources.

Key players to watch

Who might be the first teams to implement these innovations before they become the new gold standard for sustainable DEXs?

Personally, I am optimistic about those on-chain DEXs that have high demand and adoption rates for AI Agents, such as Jupiter and SynFutures. Of course, the newly emerging Hyperliquid cannot be ignored.

The integration of AI Agents into DeFi, especially sustainable DEX, is not just an incremental improvement, it represents 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, platforms that adopt these innovations early can position themselves as pioneers of the DeFAI movement.