Repeatability
Medium
The general pattern — identify full-table scans, add indexes, rewrite queries — is repeatable, but the specific optimizations depend heavily on the schema, query patterns, and data distribution, which vary per instance. Each engagement requires fresh analysis.
Ambiguity Tolerance
Medium
The goal (reduce query time from 3–5s to something acceptable) is directionally clear, but 'appropriate' indexes and 'optimized' query logic are judgment calls. There's no hard SLA stated, and success criteria like acceptable latency thresholds are undefined.
Data & Tool Availability
Low
The agent needs access to the actual Java source code, the PostgreSQL schema, live query plans (EXPLAIN ANALYZE output), and ideally a staging environment to benchmark before/after. Without these, it can only produce hypothetical recommendations, not verified results.
Error Cost
High
A poorly chosen index can degrade write performance or consume significant disk space; a rewritten query with a subtle bug could return incorrect results in production. Changes to a production microservice carry real risk and require careful review and rollback capability.
Human Judgment Required
Medium
Choosing between index types (B-tree vs. GIN for full-text search), deciding whether to denormalize, and evaluating trade-offs between read and write performance require engineering judgment. An experienced developer should validate any AI-generated recommendations before deployment.