yc-bench/system_design/02_data_models.md

Data Models & Database Design

Location: src/yc_bench/db/

Design Choice: SQLAlchemy ORM with SQLite

The benchmark uses SQLAlchemy's declarative ORM over SQLite for several reasons:

1. Single-file persistence: SQLite stores the entire game state in one file, making runs portable and inspectable
2. Transactional safety: ACID guarantees prevent partial state updates
3. Query flexibility: SQL allows complex queries for financial reports, task filtering, etc.
4. Dual-backend support: The same ORM works with PostgreSQL via DATABASE_URL environment variable for production/scaling scenarios

Schema Overview

┌──────────────┐     ┌───────────────────┐
│   Company    │────<│  CompanyPrestige   │  (1 per domain × company)
└──────┬───────┘     └───────────────────┘
       │
       ├────<┌──────────────┐     ┌──────────────────┐
       │     │   Employee   │────<│ EmployeeSkillRate │  (1 per domain × employee)
       │     └──────┬───────┘     └──────────────────┘
       │            │
       │            │    ┌────────────────┐
       │            └───<│ TaskAssignment  │  (employee ↔ task junction)
       │                 └────────┬───────┘
       │                         │
       ├────<┌──────────┐────────┘
       │     │   Task   │────<┌─────────────────┐
       │     └────┬─────┘     │ TaskRequirement  │  (1 per domain × task)
       │          │           └─────────────────┘
       │          │
       │          └────>┌──────────┐
       │                │  Client  │  (task issuer with hidden multiplier)
       │                └────┬─────┘
       │                     │
       ├────<┌───────────────┘
       │     │  ClientTrust  │  (company ↔ client trust level)
       │     └──────────────┘
       │
       ├────<┌──────────────┐
       │     │  SimEvent    │  (discrete events queue)
       │     └──────────────┘
       │
       ├────<┌──────────────┐
       │     │ LedgerEntry  │  (financial transactions)
       │     └──────────────┘
       │
       ├────<┌──────────────┐
       │     │  SimState    │  (simulation clock & counters)
       │     └──────────────┘
       │
       └────<┌──────────────┐
             │  Scratchpad  │  (agent persistent memory)
             └──────────────┘

Model Details

Company (models/company.py)

Column	Type	Notes
id	UUID (PK)	Auto-generated
name	String	Company name
funds_cents	BigInteger	Financial balance in cents

Design choice: Funds stored in cents (integer) to avoid floating-point rounding errors in financial calculations. BigInteger supports very large/negative values.

CompanyPrestige (models/company.py)

Column	Type	Notes
company_id	UUID (FK)	References Company
domain	String	research / inference / data_environment / training
prestige_level	Float	Range [1.0, 10.0]

Design choice: Prestige is tracked per-domain rather than as a single score. This forces specialization trade-offs and creates a 4-dimensional progression space.

Employee (models/employee.py)

Column	Type	Notes
id	UUID (PK)	Auto-generated
company_id	UUID (FK)	References Company
name	String	Employee name
tier	String	junior / mid / senior
work_hours_per_day	Float	Hours available per business day
salary_cents	BigInteger	Monthly salary in cents

EmployeeSkillRate (models/employee.py)

Column	Type	Notes
employee_id	UUID (FK)	References Employee
domain	String	One of 4 domains
rate_domain_per_hour	Float	Work units produced per hour

Design choice: Skill rates are hidden from the agent. The agent sees tier and salary but not per-domain effectiveness. This creates an information asymmetry puzzle -- the agent must infer employee strengths from task outcomes.

Task (models/task.py)

Column	Type	Notes
id	UUID (PK)	Auto-generated
company_id	UUID (FK, nullable)	NULL = market task, set on acceptance
status	Enum	market → planned → active → completed_success / completed_fail / cancelled
title	String	Task description
required_prestige	Float	Minimum prestige needed in ALL task domains
reward_funds_cents	BigInteger	Payment on successful completion
reward_prestige_delta	Float	Prestige gained per domain on success
skill_boost_pct	Float	Employee skill rate increase on success
accepted_at	DateTime (nullable)	When task was accepted from market
deadline	DateTime (nullable)	Calculated at acceptance
completed_at	DateTime (nullable)	When task finished
success	Boolean (nullable)	True = on-time, False = late
progress_milestone_pct	Float	Tracks progress milestones (e.g., 50%)

Design choice: company_id being nullable elegantly distinguishes market tasks (available for browsing) from accepted tasks (owned by the company).

TaskRequirement (models/task.py)

Column	Type	Notes
task_id	UUID (FK)	References Task
domain	String	Which domain this requirement covers
required_qty	Float	Total work units needed
completed_qty	Float	Work units completed so far

Design choice: Multi-domain requirements make tasks a multi-dimensional optimization problem. A task might need work in 2-4 domains simultaneously.

TaskAssignment (models/task.py)

Column	Type	Notes
task_id	UUID (FK)	References Task
employee_id	UUID (FK)	References Employee
assigned_at	DateTime	When assigned

Design choice: Many-to-many junction table. An employee can work on multiple tasks (throughput splits), and a task can have multiple employees (parallel progress).

Client (models/client.py)

Column	Type	Notes
id	UUID (PK)	Auto-generated
name	String(255)	Client company name (e.g. "Nexus AI")
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: 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)

Column	Type	Notes
company_id	UUID (FK, PK)	References Company
client_id	UUID (FK, PK)	References Client
trust_level	Numeric(6,3)	Range [0.0, 5.0], default 0.000

Design choice: Composite primary key (company_id, client_id) — one trust level per company-client pair. Trust affects both reward scaling and work reduction. See 11_client_trust.md for full mechanics.

SimEvent (models/event.py)

Column	Type	Notes
id	UUID (PK)	Deterministic (uuid5)
company_id	UUID (FK)	References Company
event_type	String	task_completed / bankruptcy / task_half / horizon_end
scheduled_at	DateTime	When event triggers
payload	JSON	Event-specific data
dedupe_key	String	Prevents duplicate events
consumed	Boolean	True after processing

LedgerEntry (models/ledger.py)

Column	Type	Notes
id	UUID (PK)	Auto-generated
company_id	UUID (FK)	References Company
occurred_at	DateTime	Transaction timestamp
category	Enum	MONTHLY_PAYROLL / TASK_REWARD / TASK_FAIL_PENALTY / TASK_CANCEL_PENALTY
amount_cents	BigInteger	Signed amount (negative = cost)
ref_type	String (nullable)	Reference entity type
ref_id	UUID (nullable)	Reference entity ID

Design choice: Immutable append-only ledger provides a complete financial audit trail. No entries are ever deleted or modified.

SimState (models/sim_state.py)

Column	Type	Notes
company_id	UUID (FK, PK)	References Company
sim_time	DateTime	Current simulation clock
run_seed	Integer	RNG seed for reproducibility
horizon_end	DateTime	When simulation ends
replenish_counter	Integer	Tracks market task replenishment

Scratchpad (models/scratchpad.py)

Column	Type	Notes
company_id	UUID (FK)	References Company
content	Text	Free-form agent notes

Design choice: Scratchpad survives LLM context truncation, giving the agent persistent memory across the full simulation.

Session Management (session.py)

session_scope(factory) → context manager

• Creates a scoped session with automatic commit/rollback
• Supports both SQLite (default) and PostgreSQL (via DATABASE_URL)
• init_db() creates all tables from ORM metadata

Design choice: Context manager pattern ensures every database operation is properly transacted, preventing partial state updates that would corrupt the simulation.