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Updated design mds

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Muyu He 2026-03-19 18:39:57 -07:00
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system_design/02_data_models.md
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@ -146,11 +146,12 @@ The benchmark uses SQLAlchemy's declarative ORM over SQLite for several reasons:
|--------|------|-------|
| `id` | UUID (PK) | Auto-generated |
| `name` | String(255) | Client company name (e.g. "Nexus AI") |
| `reward_multiplier` | Float | Hidden per-client bonus [0.7, 2.5], not shown to agent |
| `reward_multiplier` | Float | Per-client reward factor [0.7, 2.5] (currently unused in reward calculation) |
| `tier` | String(32) | Agent-visible label: Standard / Premium / Enterprise |
| `specialty_domains` | JSON | List of 1-2 domain strings (e.g. ["research", "training"]) |
| `loyalty` | Float | Hidden loyalty score [-1.0, 1.0]. RAT clients (< -0.3) cause scope creep |
**Design choice**: The `reward_multiplier` is hidden from the agent; only `tier` is visible. This prevents trivially optimal strategy (always pick highest multiplier) and requires the agent to experiment and observe payouts.
**Design choice**: `loyalty` is hidden from the agent. RAT clients secretly inflate task work after acceptance (scope creep), causing deadline failures. The agent must detect RATs by observing per-client failure patterns via `client history`.
### ClientTrust (`models/client.py`)