This company provides a protocol for building and managing networks of AI agents that can work together. Their platform uses deterministic engines and AI decision-making, secured with zk-proofs and trusted hardware, to enable scalable and secure AI collaboration within blockchain environments.
Funding
Funding not disclosed
Founders
Product
Problem
Current smart contracts on blockchains lack the flexibility and adaptability to handle complex, real-world scenarios, requiring extensive manual intervention and increasing security vulnerabilities. Coordinating actions across multiple chains and integrating off-chain data introduce further complexities and limitations.
Solution
Omo Protocol provides a multi-agent orchestration layer that enables developers and users to build, deploy, and scale cooperative AI agent networks that can seamlessly interact with on-chain primitives. The platform leverages both deterministic automation engines and AI-driven decision-makers, secured by zk-proofs and trusted hardware, to ensure untampered AI inference. Omo allows for cross-chain coordination and supports various AI agent stacks, facilitating the creation of AI-powered products and the automation of complex processes. By minimizing the reliance on smart contracts and utilizing secure enclaves for key encryption, Omo reduces attack vectors and enhances the security of on-chain operations.
Target Audience
The primary users are Web3 developers, DeFi projects, and enterprises looking to automate complex on-chain processes, delegate voting power, and deploy cross-chain strategies with enhanced security and flexibility.
Features
- Multi-framework support for building with any AI agent stack
- Verified machine learning inference using zero-knowledge proofs and trusted hardware
- Cross-chain coordination for orchestrating agent networks across multiple virtual machines and blockchains
- OmoChat for executing on-chain transactions via text
- Trusted execution environments for agent key encryption
- TypeScript SDK for integration and coordination across agent frameworks