BAYSED
ai agent memory hub
The problem BAYSED solves
Current AI agents lack persistent memory—they reset after each session, losing context, knowledge, and value. This means every user must repeatedly retrain or re-prompt agents, wasting significant time and resources. BAYSED solves this by providing:
- Persistent AI Memory: Continuously maintain agent context and knowledge across interactions.
- Collaborative and Shared AI: Users can share sophisticated, pre-trained AI agents, significantly reducing redundant efforts.
- Monetizable and Programmable IP: Memories can be traded, creating monetization opportunities and incentives to continuously enhance AI quality and value.
BAYSED ultimately makes AI agents reusable, composable, and more powerful, vastly improving efficiency and accessibility across different AI-driven
Challenges we ran into
One significant challenge was integrating secure, decentralized access control with persistent AI memory storage. Initially, managing encryption and permissions securely without compromising ease-of-use proved difficult. We overcame this by:
- Separating concerns clearly between Walrus (persistent storage) and Tusky (encryption & access control), enabling modular, secure, and maintainable systems.
- Leveraging Story Protocol’s MCP (Module and Composable IP) for tracking complex ownership and licensing issues. This integration provided robust, traceable IP management, rewarding creators transparently each time their memory was enhanced.
This combination effectively addressed security concerns and incentive alignment, creating a reliable yet flexible ecosystem.
Tracks Applied (3)
General Track
Create Your Agentic Future
Nethermind
Agent on Story
Story