Repeatability
High
Schema-to-ORM generation is a deterministic, pattern-driven task: each table maps to a class, each column to a typed attribute, each foreign key to a relationship. The structure is the same every time, making it highly automatable.
Ambiguity Tolerance
Medium
The schema defines columns and foreign keys precisely, but success criteria for relationship naming conventions, lazy-loading strategies, and async pool tuning are not fully specified. A human must define or approve those conventions before the output is production-ready.
Data & Tool Availability
Medium
The agent needs live read access to the PostgreSQL schema (via pg_catalog or information_schema) and knowledge of which 35 tables are 'most used' — that usage ranking is not self-evident from the schema alone and must be supplied by the user.
Error Cost
Medium
Incorrect relationship mappings or misconfigured async pools could cause runtime bugs or data integrity issues in production, but the output is code that can be reviewed and tested before deployment, making errors reversible with standard dev practices.
Human Judgment Required
Medium
Naming conventions, cascade behavior, eager vs. lazy loading choices, and connection pool sizing involve architectural judgment that depends on app-specific usage patterns the agent cannot infer from schema alone. A developer review pass is necessary.