What I Built
AgentGuard is basically a trust and insurance system for AI agents that spend money. Imagine your AI shopping assistant buying groceries for you, but what happens when it accidentally orders 50 pounds of bananas instead of 5? Right now, merchants are terrified of this because they don't know if they'll get hit with chargebacks. I built this as a platform where agents put up a bond (like a security deposit) before they can spend, and if they mess up, that bond covers the refund instead of the merchant losing money. It's like giving AI agents a credit score and insurance policy at the same time.
The "Aha!" Moment
I was reading about how payment companies are panicking over AI agents making purchases, and it suddenly clicked: this is a massive coordination problem. Users won't trust agents with their money without protection. Merchants won't accept agent payments without protection from chargebacks. And nobody can agree on who's liable when an agent hallucinates and buys something weird. The current system just isn't built for a world where software makes autonomous financial decisions. I realized that if we could make agent transactions safer than regular credit card purchases for merchants, they'd actually prefer them. That's when I knew I had something.
How We Built It
The core idea is simple: when you create an AI agent, you define its rules (like "only buy groceries under $200/week from Whole Foods"). The agent stakes MNEE tokens as insurance before it can transact. When it wants to buy something, the code check if it follows the rules, lock the payment in escrow for 24 hours, and if nobody complains, the merchant gets paid. If you dispute it, Gemini AI analyzes everything—the agent's rules, what it actually bought, your complaint, and the merchant's side—and decides who is right in under a minute. The smart part is that good agents build reputations over time and pay lower fees, while bad agents either improve or get priced out. I built smart contracts to handle the money movement on Ethereum, hooked up Gemini's API for the dispute arbitration, and created a dashboard so you can see everything happening in real-time. The whole system runs without anyone in the middle making judgment calls—it's all automated through code and AI.
Challenges I Hit
My biggest headache was figuring out the bond economics. If bonds are too high, nobody will use agents. Too low, and there's not enough insurance to cover disputes. I ran the math: $$\text{Bond} = \frac{\text{Monthly Limit}}{2} \times \frac{1000 - \text{Reputation}}{1000}$$. It had to scale with both spending power and trustworthiness. Another problem was handling edge cases in disputes—what if an agent buys the right product but the wrong color? Is that a 50% refund or 100%? I realized I couldn't code every scenario, so I built an in-DAO voting system for the genuinely unclear cases. I had to completely restructure how data gets stored on-chain versus off-chain and get AI arbitration to be consistent and fair. Finally, I also needed to consider using a low-cost blockchain because Ethereum is expensive, so I built this for a test environment using the Base Sepolia testnet just for the testing, and I deployed a mock token to mimic the MNEE token. I also proposed a bridging of MNEE tokens to the Base network or other L2 blockchains to make agent commerce and payments less expensive.
What I Learned
The biggest lesson: solving coordination problems is way more valuable than building another payment rail. Everyone's racing to let AI agents make payments, but nobody's solving the "what if it breaks?" question. I also learned that incentive design is everything—the reputation bond system works because it literally pays agents to be trustworthy. Good behavior = lower fees = more profit. No enforcement needed. On the technical side, I learned that blockchain is actually perfect for this because you need that transparency and immutability for disputes. You can't have a centralized company deciding who's right when there's money involved—people don't trust that. But code + AI + community voting? That works. The wildest realization, though, was that this could actually flip the merchant economics—agent transactions could be more profitable than credit cards because there's no chargeback risk. If I am right about that, merchant adoption could happen fast. This started as a simple idea for this hackathon but honestly feels like something the industry actually needs right now.
What's next for AgentGaurd
The big vision? I want this project to become the standard authorization layer that every AI agent uses before making a purchase. I will also build an SDK so developers can easily integrate their AI agents with AgentGuard (like a Stripe for agent payments). Think about it, in 5-10 years, millions of AI agents will be transacting daily, and they'll all need reputation scores and insurance. I am positioning this project to be the infrastructure that powers that. If we can get one major player to integrate AgentGuard, the network effects kick in hard. This could legitimately become the trust layer for the entire agent economy.
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