AgentNLQ: A General-Purpose Agent for Natural Language to SQL
The article discusses a new multi-agent method for converting natural language to SQL, known as AgentNLQ. This method achieves a semantic accuracy of 78.1% on the BIRD benchmark, addressing the gap between machine and human SQL writing capabilities. Key innovations include an optimized orchestrator and an advanced schema enrichment method to enhance accuracy across various domains.
- ▪AgentNLQ is a general-purpose agent designed for natural language to SQL conversion.
- ▪The method achieves 78.1% semantic accuracy on the BIg Bench for LaRge-scale Database benchmark.
- ▪It incorporates user-provided schema and business rules to generate accurate SQL queries.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19010 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | bdr6hXBLO73e |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.19010 (cs) [Submitted on 18 May 2026] Title:AgentNLQ: A General-Purpose Agent for Natural Language to SQL Authors:Olena Bogdanov, Yeunji Jung, Chandra Dhir, Pareekshitreddy Gaddam, Saurabh Jain, Lakshmi Tumati, Vijay Parthasarathy, Anup Shirgaonkar View a PDF of the paper titled AgentNLQ: A General-Purpose Agent for Natural Language to SQL, by Olena Bogdanov and 7 other authors View PDF HTML (experimental) Abstract:Natural language to SQL (NL2SQL) conversion is an important problem for researchers and enterprises due to the ubiquitous importance of relational databases in broad-ranging practical problems.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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