Overview
AIUSD is a trading platform built for both human users and AI agents. Humans can use AIUSD through first-party interfaces and chat-based experiences. AI agents can use AIUSD Skills (either the direct MCP-based skill or the natural language skill) or connect directly through the MCP Hub API. All paths converge on the same execution platform.
At a high level, the platform combines four layers:
- User and agent interfaces
- A shared orchestration layer for intent handling
- Core services for balances, custody, and trading
- On-chain execution and institutional custody
This page explains the platform at a high level. It intentionally omits sensitive implementation details such as key derivation formulas, signing paths, wallet inventories, and internal operational thresholds.
Core components
| Component | Role |
|---|
| User interfaces | Serve human users through web and chat-style experiences |
AIUSD Skills | Let external AI agents access the platform through packaged integrations (direct MCP skill or natural language skill) |
MCP Hub | Exposes a tool surface for balances, funding, and trading |
| Ledger | Tracks AIUSD balances, staking balances, and movement between reserve and trading contexts |
| TIM | Interprets trading intent and routes execution to the right chain and venue |
| Relayer and chain adapters | Handle chain-specific transaction execution |
| Indexers and monitoring | Detect deposits, observe on-chain state, and support operational controls |
CEFFU | Holds reserve assets in institutional custody |
Account model
The platform separates identity, reserve custody, and trading execution.
| Account type | Purpose |
|---|
| Login wallet | Proves user identity during authentication |
| Deposit address | Receives supported stablecoin deposits into the AIUSD system |
| Trading account | Holds tradable on-chain assets and executes user-authorized trades |
| Hot wallet | Supports operational transfers and fulfillment |
CEFFU custody account | Holds reserve assets backing liquid AIUSD |
CEFFU staking account | Holds reserve assets backing staked AIUSD |
AIUSD ledger balances are ledger liabilities inside the reserve modelOn-chain trading balances are execution balances outside the AIUSD reserve model until they are converted back through ledger operations
This separation lets the platform support natural-language trading without confusing reserve assets with active trading positions.
Custody and reserve management
Reserve assets are managed in CEFFU custody. Liquid reserves and staked reserves are separated at the custody layer.
The custody model is designed around clear fund boundaries:
- Liquid AIUSD reserves are held in
CEFFU custody
- Staked reserves are isolated in
CEFFU staking
- Hot wallets support operational movement and fulfillment
- Trading accounts are used for execution, not for reserve backing
We run continuous reconciliation and operational monitoring across custody balances, ledger state, and execution flows. This architecture doc does not expose internal reconciliation procedures.
How intent becomes execution
The same execution model supports a human asking for a trade and an AI agent fulfilling that trade on the user’s behalf.
In practice, the flow is:
- The user authenticates with a wallet-based identity flow.
- The platform reads the user’s ledger balances and trading-account balances.
- If needed, the platform moves value into the correct execution context.
TIM executes the trading intent on the destination chain or venue.- The platform returns execution status and updated balances.
This model is especially important for AI agents. Agents can use AIUSD Skills or the MCP Hub API directly to fulfill user trading intents without bypassing the platform’s custody and balance controls.
Security model
The platform uses a wallet-based identity model and a separate trading-account control model.
- A user’s login wallet proves identity
- Per-user trading accounts are deterministically mapped from authenticated user identity inside a secure key-management boundary
- The application does not store those signing keys as plaintext in the application database
- Signing and execution occur through controlled service boundaries
This approach lets the platform give users persistent trading accounts while avoiding a simple “private key in database” design.
Design principles
- One platform for both human users and AI agents
- Clear separation between reserve custody and trading execution
- Natural-language intent as the user-facing interaction model
- Shared backend controls for funding, gas, execution, and monitoring
- High-level transparency without exposing sensitive implementation details
