Repeatability
Medium
The structural pattern — batched UPDATE with rollback safety, progress logging, index hints — is repeatable and well-understood. But the specific derivation logic for `status_updated_at` from transaction history is unique to this schema and business domain, requiring one-time judgment.
Ambiguity Tolerance
Low
Success criteria sound clear (backfill 2.3M rows correctly) but the actual definition of 'correct' depends on undisclosed business rules: which transaction event sets the timestamp, how ties are broken, what NULLs mean. Without these, the agent cannot know if the script is right.
Data & Tool Availability
Low
The agent almost certainly lacks access to the live schema, transaction table structure, existing indexes, row volume distribution, and production DB credentials. It can write a template, but cannot validate or test against the real environment.
Error Cost
High
A bug in a backfill on 2.3M live production rows can corrupt customer data, cause prolonged table locks, or silently write wrong timestamps that propagate into downstream systems. Even with rollback safety, partial execution or lock contention can cause real outages.
Human Judgment Required
High
Deciding the correct business logic for the timestamp derivation, choosing safe batch sizes for this specific production load, and making the call to execute on a live system all require a human engineer who understands the system's risk tolerance and operational context.