Agentic Omnichain
Music Platform
Sidebar unifies music discovery and venture-style investment. Powered by conversational AI and the Fairshare token, we are building the liquidity layer for fractional music copyrights.
1. Executive Summary
This paper outlines the architecture for a decentralized, AI-driven music exchange. By merging natural language processing, retrieval-augmented generation (RAG), and omnichain smart contracts, the Sidebar platform unifies music discovery and venture-style investment.
Autonomous AI agents act as personalized A&R representatives, facilitating the seamless discovery, valuation, and acquisition of fractional music copyrights represented as Omnichain Fungible Tokens (OFTs) via the Fairshare ecosystem.
2. Challenges & Solutions
| Challenge | Sidebar Solution | Complexity (1-5) |
|---|---|---|
| The Stablecoin Contradiction | Dual-Token Architecture: Separate the volatile Fairshare copyright token from the settlement stablecoin (USDC). | 2 |
| Market-Making Capital Drain | AI-Optimized Permissioned AMMs: Agents optimize concentrated liquidity bounds in KYC-gated pools based on real-time stream metrics. | 4 |
| Unregistered Securities Trap | ERC-3643 Standard & Stripe Identity: SPV wrappers utilizing on-chain Identity Registries and frictionless checkout KYC. | 5 |
3. Platform Architecture
To maintain a frictionless consumer experience while managing complex financial transactions, Sidebar is strictly decoupled into distinct operational layers.
Solution Architecture
The high-level business logic and value flow of the Fairshare ecosystem.
Deployment Infrastructure
Omnichain interoperability ensuring liquidity is not fragmented.
4. Security & UX Report
User Experience
- ▹ "Lazy" Stripe Onboarding via native Web3 widgets.
- ▹ Gas Abstraction (ERC-4337) to eliminate native gas token friction.
- ▹ Chain-Agnostic Yield via LayerZero's lzCompose.
Security
- ▹ The "Air Gap": AI agents are strictly sandboxed from private keys.
- ▹ ERC-3643 Permissioned Compliance and Identity Registries.
- ▹ Deterministic LLM fallbacks via FastAPI Pydantic models.