Repeatability
High
SQL optimization follows well-established patterns — join ordering, index usage, materialized view design — that are structurally consistent across instances. The agent applies the same analytical framework each time, making this highly repeatable.
Ambiguity Tolerance
High
Success is objectively measurable: queries must run in under 30 seconds on PostgreSQL 14. There is no subjective taste involved, and the pass/fail criterion is unambiguous.
Data & Tool Availability
Medium
The agent needs the existing raw SQL queries, schema definitions, and ideally EXPLAIN ANALYZE output to reason about bottlenecks. Without direct database access or query plans, the agent must work from provided artifacts, which limits its ability to validate performance claims before deployment.
Error Cost
High
Incorrect view definitions or schema changes on a production e-commerce database could corrupt report data, break dependent application code, or cause downtime. Schema modifications are not trivially reversible without a rollback plan, so errors carry real business risk.
Human Judgment Required
Low
This is a technical optimization problem with deterministic correctness criteria. A DBA should review and test the output before deploying, but the core reasoning — query rewriting, index selection, materialization strategy — does not require human intuition or relationship context.