Use Cases

1. Strategy Protection for On-Chain Traders

Traders can encode their strategies into zk-powered AI agents that execute across decentralized markets. These agents operate without revealing parameters, triggers, or timing — shielding you from reverse engineering, copy trading, and competitive analysis.

  • Example: a user deploys a mean-reversion bot across ETH/USDC pairs, but no one knows when or why it enters/exits positions.

2. Front-Running and MEV Resistance

Using stealth order flow and zero-knowledge execution, agents avoid exposure to mempool bots and frontrunners. Trades appear on-chain after execution, with no exploitable signals in advance.

  • Example: an arbitrage agent identifies a price gap across private liquidity pools, executes, and settles — all while staying hidden from MEV actors.

3. DAO Treasury Automation

DAOs can use autonomous agents to rebalance treasuries, farm yield, or hedge volatility without exposing sensitive strategies or internal logic to competitors, voters, or adversarial watchers.

  • Example: a DAO rebalances 5% of its treasury into stablecoins monthly based on a private volatility model, all managed by an invisible on-chain agent.

4. Private Yield Optimization

Users can deploy agents that automatically seek out the highest-yielding private vaults, execute swaps, and reinvest earnings — without revealing wallet activity or yield behavior.

  • Example: a private wallet earns yield via stablecoin rotation between shielded vaults on Layer 2, avoiding copycats or trend followers.

5. Encrypted DeFi Strategy Backtesting

Developers and quant traders can test their logic on historical market data within an encrypted compute environment, then convert successful models into live autonomous bots.

  • Example: a machine-learning-based trend strategy is trained and evaluated on private datasets, then deployed via zk-proven agent logic.

6. Institutional-Grade Market-Making

Private market makers can operate across DEXs without exposing liquidity positions, order book behavior, or execution timing — ensuring sustainable, confidential market presence.

  • Example: an institution provides stablecoin liquidity to shielded pools using an AI agent that rebalances depth and range silently over time.

7. Protocol-Integrated AI Bots

Protocols can embed autonomous trading agents into their platforms as private liquidity managers, incentive balancers, or execution bots — offering users built-in automation with full confidentiality.

  • Example: a DEX integrates trading agents to rebalance LP pools based on usage metrics and price bands without disclosing vault states.

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