AgenticSQL: A Modular NL2SQL Approach

• AgenticSQL is defined as a modular NL2SQL paradigm that decomposes query translation into specialized agents for planning, generation, and verification.
• It leverages large language models and iterative feedback to improve execution accuracy, scalability, and interpretability across diverse databases.
• Representative systems show significant enhancements in performance by employing multi-agent orchestration and consensus-based refinement techniques.

AgenticSQL refers to a paradigm and set of methodologies for translating natural language (NL) queries into SQL (NL2SQL) using agent-based, modular, and orchestrated computational structures. This approach leverages LLMs and agentic workflows to achieve robust, scalable, and interpretable semantic parsing across a wide spectrum of domains and database schemas. AgenticSQL frameworks depart from monolithic, single-pass NL2SQL architectures by decomposing reasoning, planning, generation, and verification into specialized agents or modules with explicit data flow, inter-agent communication, and iterative refinement. The intent is to maximize execution accuracy, robustness, diversity, interpretability, and extensibility across heterogeneous databases and complex query types.

1. Architectural Patterns for AgenticSQL

AgenticSQL systems commonly feature decomposition of the NL2SQL task into a pipeline of interacting agents—either as distinct modules (multi-agent systems) or roles internal to an orchestrated agent. Typical architectural motifs include:

• Planning–Generation Pipelines: Separate planners generate stepwise natural language (NL) plans or sub-tasks, which are then synthesized into SQL by specialized generator agents. The OraPlan-SQL system exemplifies this, featuring a Planner Agent for structured NL decomposition and an SQL Agent for executable translation. Empirically, this decoupling yields execution accuracy (EX) improvements and enables targeted feedback-driven prompt engineering (Liu et al., 27 Oct 2025).
• Multi-Expert or Modular Agents: Systems such as AGENTIQL implement multiple expert agents—e.g., reasoning agents for decomposition and table selection, coding agents for sub-query generation, and column selectors for refinement. An adaptive router determines whether a modular agentic pipeline or a baseline parser offers better trade-off for a given query-schema instance based on learned or heuristic metrics (Heidari et al., 12 Oct 2025).
• Orchestration Layers and Tool-Enhanced Agents: Agentic frameworks frequently employ orchestration layers where an agent coordinates a set of tools for schema inspection, SQL generation, execution, result visualization, and error handling. The integration of external retrievers, detectors, and reasoning tools improves system reliability, particularly for spatio-temporal queries and real-world enterprise tasks (Redd et al., 29 Oct 2025, Wang et al., 30 Aug 2024).
• Multi-Round/Consensus Agents: AgenticSQL pipelines utilize iterative refinement and consensus mechanisms—sampling diverse candidate plans or queries, executing each, and selecting results via voting or verification. These mechanisms enhance robustness in ambiguous or multi-modal scenarios and are coupled with meta-prompting strategies for continual planner improvement (Liu et al., 27 Oct 2025).
• Cooperative Multi-Agent Protocols: In settings with segmented or distributed schemas, such as CSMA, each agent only sees part of the database schema, and agents interact to collectively assemble sufficient schema knowledge for SQL synthesis and cross-verification, supporting privacy preservation and federated database access (Wu et al., 8 Dec 2024).

2. Core Methodological Components

AgenticSQL is characterized by explicit task decomposition, modularity, and feedback-driven adaptation.

1. Schema Grounding and Linking: This can be performed by schema linking agents (SQL-of-Thought) or schema inspection tools (Agentics), pruning irrelevant tables and columns via explicit reasoning or semantic similarity (Chaturvedi et al., 30 Aug 2025, Gliozzo et al., 21 Aug 2025).
2. Task Decomposition: Complex queries are decomposed into logical sub-tasks—such as subproblem identification (clause-level), question decomposition, or pipeline planning (Heidari et al., 12 Oct 2025, Chaturvedi et al., 30 Aug 2025).
3. Iterative Planning and Generation: Planners generate high-level or stepwise plans in natural language (possibly with explicit guidelines or meta-prompts), which are converted into executable SQL by subordinate agents or modules. Feedback-guided meta-prompting and plan diversification enhance both output diversity and generalization (Liu et al., 27 Oct 2025).
4. Answer Verification and Guided Correction: Execution feedback, error taxonomies, retrievers, and structural detectors are employed to diagnose and correct SQL errors. Correction agents use chain-of-thought (CoT) reasoning over error codes to produce targeted refinements, surpassing static correction loops (Chaturvedi et al., 30 Aug 2025, Wang et al., 30 Aug 2024).
5. Memory and Dialogue Coherence: In conversational or multi-turn scenarios, such as MTSQL-R1, agents maintain a persistent memory of previous queries, outputs, and intermediate constraints, enabling memory-guided refinement and coherence checking (Guo et al., 12 Oct 2025).
6. Token-Efficiency and Scalability: AgenticSQL systems such as Datalake Agent reduce computational costs by interactively acquiring only necessary schema information, avoiding monolithic prompt overloading and thus achieving up to 87% token savings with comparable performance (Jehle et al., 16 Oct 2025).

3. Representative Systems and Algorithms

System	Key Modules/Agents	Distinctive Features	Benchmark Highlights
OraPlan-SQL	Planner, SQL Agent	Feedback-guided meta-prompting, entity linking, plan diversification	EX=55.0% (EN), 56.7% (ZH) Archer
Datalake Agent	Agentic loop with selective info	Token-efficient, agentic API actions	87% token reduction; EX~75% on 319 tables
SQL-of-Thought	Schema linker, subproblem, planner, guided correction agents	Taxonomy-guided dynamic error correction	SOTA EX=91.59% (Spider)
AGENTIQL	Reasoning, coding, column selection agents, adaptive router	Modular pipeline, parallel sub-query, interpretable output	EX=86.07% (Spider, 14B)
RubikSQL	RAG, SQL Gen, Refine agents; KB-indexing	Agentic lifelong KB for enterprise NL2SQL	EX=77.3% (BIRD), 58.9% (KaggleDBQA)
CSMA	Schema extraction/gen/check agents	Cooperative segmentation, privacy	Matches full-schema baselines Spider/BIRD
Agentics	Transducing agents via type logic	Composable algebraic flow, schema abstraction	+10.3 EM over base on BIRD-dev

These systems implement advanced algorithms involving meta-prompt updates, iterative plan-vote-execute cycles, stateless logical transductions (as in Agentics), and graph-based or memory-augmented reasoning (Gliozzo et al., 21 Aug 2025, Liu et al., 27 Oct 2025, Jehle et al., 16 Oct 2025, Chaturvedi et al., 30 Aug 2025, Heidari et al., 12 Oct 2025, Chen et al., 25 Aug 2025, Wu et al., 8 Dec 2024).

4. Evaluation Metrics, Empirical Performance, and Scaling Properties

AgenticSQL systems are evaluated on canonical tasks such as Spider, BIRD, DataBench, KaggleDBQA, and domain-specific datasets. Key metrics include execution accuracy (EX), exact match (EM), and task-specific validity or efficiency scores.

• Execution Accuracy (EX):
  • OraPlan-SQL attained 55.0% (EN) and 56.7% (ZH) EX on Archer, surpassing the second-best system by 6-12.6 points (Liu et al., 27 Oct 2025).
  • SQL-of-Thought reached 91.59% EX on Spider and 90.16% on Spider-Realistic via multi-agentic planning and error correction (Chaturvedi et al., 30 Aug 2025).
  • AGENTIQL with adaptive routing achieved EX=86.07% using 14B models on Spider test (Heidari et al., 12 Oct 2025).
  • Datalake Agent maintained EX~75% with an 87% reduction in token usage (scaling to hundreds of tables) (Jehle et al., 16 Oct 2025).
  • RubikSQL reported EX=77.3% (BIRD mini-dev, n=8 sampling), and 75.9% in single-shot on BIRD, setting new SOTA for industrial NL2SQL (Chen et al., 25 Aug 2025).
• Token/Compute Efficiency: Agents that employ explicit information fetching loops (Datalake Agent, Agentics) can dramatically reduce computational cost while maintaining accuracy, crucial for enterprise data lake environments.
• Interpretability and Modular Debugging: Agentic decomposition enables exposure of intermediate plans and sub-queries for inspection and targeted debugging, supporting transparent and trustworthy deployment (Heidari et al., 12 Oct 2025, Chaturvedi et al., 30 Aug 2025).

5. Key Technical Innovations and Analysis

The following innovations are salient in AgenticSQL research:

• Meta-Prompting and Feedback-Guided Adaptation: Continuous integration of corrective guidelines via failure analysis and prompt updates empirically provides the largest performance gains in planning-centric systems. A single meta-prompt with distilled guidelines can yield a +27.9 EX gain over naïve prompts (Liu et al., 27 Oct 2025).
• Plan Diversification and Voting: Generating multiple independent execution plans and synthesizing their SQL via majority vote over result sets reduces logical errors and increases robustness to prompt or model noise (Liu et al., 27 Oct 2025, Tyagi et al., 11 Sep 2025).
• Tool-Augmented Inspection and Correction: Use of retrievers (for value normalization), detectors (for constraint/join validation), and correction loops driven by explicit error taxonomy enables the system to address both execution exceptions and subtle mismatches not caught by the DBMS alone, as evidenced by Spider-Mismatch results (Wang et al., 30 Aug 2024).
• Cooperative and Distributed Schema Reasoning: CSMA shows that distributed multi-agent cooperation, with each agent holding only partial schema, can equal full-schema performance while preserving privacy—a necessity in multi-tenant or federated environments (Wu et al., 8 Dec 2024).
• Lifelong Learning and Knowledge Base Augmentation: RubikSQL’s explicit, agentic knowledge base in Unified Knowledge Format enables cumulative improvement, robust synonym handling, and expert knowledge distillation supporting both SFT and test-time query synthesis (Chen et al., 25 Aug 2025).

6. Limitations and Open Challenges

Despite empirical gains, AgenticSQL faces several challenges:

• Prompt and Model Sensitivity: Systems often require careful hyperparameter and prompt tuning per database, especially when moving to domains with unusual schemas or data types (Tyagi et al., 11 Sep 2025, Gliozzo et al., 21 Aug 2025).
• Scalability in Privacy-Constrained or Multi-Turn Contexts: Performance and efficiency trade-offs become more severe as the schema scales or when privacy restricts full schema sharing. The effectiveness of agentic protocols with large agent pools or under extreme privacy segmentation remains a research topic (Wu et al., 8 Dec 2024).
• Generalization Across New Domains: While agentic frameworks are modular and extensible, out-of-domain generalization (e.g., to databases with unseen types or complex logic) may require integrated pre-training or meta-learning extensions (Tyagi et al., 11 Sep 2025, Gliozzo et al., 21 Aug 2025).
• Computation and Latency: Tool-augmented or multi-agent iterations increase total LLM invocations per query. Strategies such as adaptive routing, test-time scaling, and cascading are needed to balance latency and accuracy (Heidari et al., 12 Oct 2025, Chen et al., 25 Aug 2025).

7. Future Directions

Emerging research avenues for AgenticSQL include:

• End-to-End Lifelong Agentic Learning: Integration of lifelong KB updates, parametric and non-parametric memory synchronization, and semantic index fusion are being pursued for industrial-scale NL2SQL automation (Chen et al., 25 Aug 2025).
• Neural-Symbolic Integration: Combining symbolic verification (e.g., cross-checks, logical plan validation) with LLM-driven agentic reasoning may further reduce hallucinations and improve robustness (Tyagi et al., 11 Sep 2025, Gliozzo et al., 21 Aug 2025).
• Conversational and Interactive Refinement: Adaptive dialogue agents that combine test-time feedback, error-driven self-correction, and user-in-the-loop supervision are likely to supplant static pipelines, providing continual improvement (Guo et al., 12 Oct 2025, Redd et al., 29 Oct 2025).
• Multi-objective Scheduling and Resource Optimization: Resource- and cost-constrained deployments demand policies that multi-objectively optimize diversity, coverage, accuracy, and latency by dynamically allocating agentic resources (Li et al., 21 Apr 2025, Jehle et al., 16 Oct 2025).

AgenticSQL thus represents a paradigmatic shift towards modular, interpretable, feedback-driven, and extensible approaches for natural language access to structured data, setting the technical and methodological foundation for robust enterprise and research NL2SQL applications across highly diverse domains (Liu et al., 27 Oct 2025, Heidari et al., 12 Oct 2025, Jehle et al., 16 Oct 2025, Chaturvedi et al., 30 Aug 2025, Chen et al., 25 Aug 2025, Wang et al., 30 Aug 2024, Gliozzo et al., 21 Aug 2025, Wu et al., 8 Dec 2024).