AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views

• URL: http://arxiv.org/abs/2604.07041v1
• Date: Wed, 08 Apr 2026 12:56:13 GMT
• Title: AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views
• Abstract summary: We introduce AV-, a framework that decomposes complex Text-to-LLMs into specialized agents.<n> AV- operates in three stages: (1) a rewriter agent compresses and clarifies the input query; (2) a view agent processes chunks to produce agentic views; and (3) a planner, generator, and revisor agent collaboratively generates the views into the final query.
• Score: 40.813123705793764
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Text-to-SQL is the task of translating natural language queries into executable SQL for a given database, enabling non-expert users to access structured data without writing SQL manually. Despite rapid advances driven by large language models (LLMs), existing approaches still struggle with complex queries in real-world settings, where database schemas are large and questions require multi-step reasoning over many interrelated tables. In such cases, providing the full schema often exceeds the context window, while one-shot generation frequently produces non-executable SQL due to syntax errors and incorrect schema linking. To address these challenges, we introduce AV-SQL, a framework that decomposes complex Text-to-SQL into a pipeline of specialized LLM agents. Central to AV-SQL is the concept of agentic views: agent-generated Common Table Expressions (CTEs) that encapsulate intermediate query logic and filter relevant schema elements from large schemas. AV-SQL operates in three stages: (1) a rewriter agent compresses and clarifies the input query; (2) a view generator agent processes schema chunks to produce agentic views; and (3) a planner, generator, and revisor agent collaboratively compose these views into the final SQL query. Extensive experiments show that AV-SQL achieves 70.38% execution accuracy on the challenging Spider 2.0 benchmark, outperforming state-of-the-art baselines, while remaining competitive on standard datasets with 85.59% on Spider, 72.16% on BIRD and 63.78% on KaggleDBQA. Our source code is available at https://github.com/pminhtam/AV-SQL.

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