Super Eliza Guard AI

Demo

About

The problem it solves The core problems this project solves: **Vaults are passive, not protective.** Most DeFi vaults can optimize yield but lack awareness or defense mechanisms when risk arises. **Billions are lost from invisible threats.** Users lose funds not from hacks, but from silent onchain risks like MEV, oracle mispricing, or liquidation cascades. **There’s no built-in real-time response.** Most protocols can’t act fast enough — human interventions or DAO governance are too slow to prevent damage. **Users assume safety, but have no guarantees.** Without automated defense, even high-yield vaults can become a liability in volatile conditions. ____________________________________________________________________ **How Super Eliza Guard AI addresses these problems:** ✅**Continuously monitors vault health and market conditions** The system runs 24/7, watching for anomalies, price drift, or patterns that signal MEV or liquidation risks. ✅**Automatically executes protective actions** When triggered, the agent can pause vaults, rebalance positions, or exit strategies — without user input. ✅**Prevents loss before it happens** Acts within milliseconds, faster than humans or bots, to neutralize threats at the source. ✅**Enhances trust and resilience** Users and protocols benefit from a smarter, self-defending vault layer — making DeFi safer, more reliable, and scalable.

How it's made

Challenges we ran into One of the biggest challenges was building a vault protection agent that actually feels autonomous — not just a button-triggered simulation. It was surprisingly difficult to simulate real-time onchain danger (e.g. MEV, price drift, oracle mismatch) in a testnet environment where prices are static and attack vectors are not naturally occurring. This made it hard to prove the value of the agent in a way that’s verifiable, repeatable, and visible to judges or users. To solve this, we designed a fully sandboxed simulation UI that mimics MEV pressure, price volatility, and vault risk — triggering the agent’s reasoning flow in a controlled environment. We also had to carefully structure state transitions, role isolation (user vs agent), and tx confirmation logs to reflect what would happen in a real-world DeFi protocol, while keeping everything fast and readable in the browser. The result: a clear, self-contained UX where the agent makes decisions, responds autonomously, and changes contract state — all in a single interactive journey. This balance between believability and testability was the hardest, but most rewarding part of the build.