{
  "id": 6299734,
  "title": "RavenDB Launches Quill to Bring Production AI Agents to Enterprise SQL Systems,No Migration Required",
  "url": "https://urgent.news/2026/09/08/ravendb-launches-quill-to-bring-production-ai-agents-to-enterprise",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-08T15:37:09.000Z",
  "source": {
    "name": "HackerNoon",
    "slug": "hackernoon",
    "url": "https://hackernoon.com/ravendb-launches-quill-to-bring-production-ai-agents-to-enterprise-sql-systemsno-migration-required?source=rss"
  },
  "original_language": "en",
  "account": "Hadera, Israel, September 8th, 2026/TechnologyWire/-- RavenDB, a NoSQL document database with over 12,000 customers, has launched Quill, a context layer for SQL databases that enables production AI agents without migration or custom AI development. As AI becomes a priority for enterprises, CTOs and engineering leaders face the challenge of integrating AI into legacy SQL systems without costly and risky modernization efforts. A Gartner survey revealed that 20% of AI initiatives fail, and only 28% achieve a positive ROI, often due to poor integration, governance, and alignment with operational needs. Oren Eini, founder and CEO of RavenDB, explains that Quill addresses this issue by providing a pre-assembled plumbing system to quickly deploy AI agents on top of existing SQL databases. Quill connects directly to an organization's SQL database and adds a context layer, allowing for production-ready agents in weeks instead of months. The system remains authoritative and the AI stack, including search and retrieval, is included. Quill supports various messaging platforms and is model-agnostic, allowing teams to use any AI model. With support for PostgreSQL, SQL Server, and MySQL, Quill can be deployed in the cloud or on-premises, meeting data-residency and regulatory requirements. For more information, visit https://ravendb.net/quill.",
  "summary": "The new context layer connects to existing SQL databases and builds a governed, model-agnostic foundation for AI agents running on live operational data, in wee",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}