Repeatability
Medium
Query optimization follows repeatable patterns (covering indexes, avoiding full scans, batching), but the specific solution depends heavily on the actual schema, data skew, and MySQL version — each instance requires unique analysis of the real query and EXPLAIN output.
Ambiguity Tolerance
Low
Success criteria sound clear (faster runtime, correct invoices) but are actually hard to verify without the real schema, sample data, and a test environment. An AI cannot confirm correctness or measure actual runtime reduction without execution access.
Data & Tool Availability
Low
The agent almost certainly lacks access to the actual MySQL schema, existing query text, table statistics, current EXPLAIN plans, and a safe test environment — all of which are essential for a non-generic optimization. Without these, output is illustrative, not deployable.
Error Cost
High
A flawed stored procedure on a nightly billing job can produce incorrect invoices for thousands of customers, corrupt financial records, or cause the job to fail silently — all of which are serious and potentially irreversible without careful rollback planning.
Human Judgment Required
Medium
Index strategy and stored procedure design are learnable patterns AI handles reasonably well, but production deployment decisions — what to test, when to cut over, how to validate billing correctness — require a DBA with context about the business and system.