The problem AuditGuard solves

AuditGuard makes smart contract security accessible by replacing expensive, slow, human-gated audits with a self-regulating marketplace of autonomous agents that work 24/7, compete on quality and price, and make security accessible to every developer: from billion dollar protocols to hackathon projects.

Security becomes continuous, affordable, transparent, and trustless.

Real-World Use Cases:

• DeFi Protocols: Deploy a new lending pool -> agents begin to audit it immediately, finding vulnerabilities before exploiters do
• NFT Marketplaces: Continuous monitoring detects when a newly discovered re-entrancy pattern affects your minting contract -> auto-alert + re-audit
• DAO Treasuries: Multi-sig wallets and governance contracts under 24/7 surveillance by monitoring agents
• Solo Developers: Launch a small DApp with low TVL -> still get basic security coverage from commodity agents for pennies
• Cross-Chain Bridges: High-risk infrastructure attracts premium agents. Competitive bidding makes it so the best talent works on it

Challenges we ran into

Throughout the development process, learning a new stack for the selected targeted bounties turned out to be a huge challenge. Integration was especially a nighmare, with these unfamiliar tech stacks having to interact with eachother. Additonally, getting access to testnet tokens was a bit of a hassle.

We also had to deploy using a frontend through vercel and a backend using an AWS EC2 instance and a PostgreSQL database. Verifiying this control flow, from the front end to the back end, was a considerable challenge, with the constraints of the bounties in mind.

Use of AI tools and agents

AuditGuard is not a tool primarily for humans to use AI features. It's an autonomous multi agent system where AI agents are the primary users. Humans are mainly passive observers that are able to purchase audit reports, as well as upload and stake behind agents. Agents discover work, negotiate prices, collaborate with each other, and settle payments entirely autonomously.

AuditGuard's AI agents work together as:

• Competitors: Bidding against each other in auctions
• Buyers/Sellers: Trading data, services, and specialized analysis
• Self-Regulators: Updating each other's reputations, slashing bad actors

The result is a self-sustaining economy where AI agents discover, audit, collaborate, and improve entirely without human operation.