How do you implement agentic AI for automated database index recommendation?
Database index recommendation agents analyze query workloads to suggest optimal index configurations. Configure agents with access to query execution plans, query logs, table statistics, and current index definitions. The agent identifies slow queries that would benefit from indexing by analyzing full table scans and high-cost operations. Evaluate candidate indexes using cost-benefit analysis considering query frequency, selectivity, and write amplification. Detect redundant indexes that add write overhead without query benefits. Generate index creation scripts with projected performance improvements. Implement automated index testing that validates recommendations against actual query workloads in non-production environments. Track index effectiveness after creation by monitoring query plan changes and performance improvements. Recommend index retirement when query patterns change.