Hint Based Query Optimization with LLM Agent and Plan Similarity

Cost based SQL optimizers remain brittle under real world data and engine diversity; small errors in cardinality or cost estimates can cascade into poor plans. We propose a conceptual architecture for hint based query optimization and describe two modules in detail: a fast module which encodes physical plans as text, embeds with a pretrained language model, retrieves nearest neighbors, and produces a compact binary hint vector with a safety check against the default plan; and an agent guided module powered by a fine tuned Large Language Model, which refines hints through iterative search with database feedback under a strict evaluation budget. We specify interfaces, data flow, and decision policies for both modules, and outline integration through standard hint mechanisms without engine changes. Preliminary module level results on JOB CEB indicate roughly twenty percent average runtime reduction for the fast module and fewer trial executions than a greedy baseline for the agent guided module. The paper focuses on architecture and module behavior rather than a single end to end deployment, so system wide testing is intentionally out of scope.