RavenDB Debuts Fully Integrated, Database-Native AI Agent Builder

Unlike the restricted embedded options in the marketplace, this first-of-its-kind function lets builders construct totally native AI brokers to show present information into customized experiences.

RavenDB, a multi-model NoSQL doc database trusted by builders and enterprises worldwide, right now introduced the launch of its AI Agent Creator, a first-to-market function totally built-in into the database, that reduces the time required to construct AI brokers from weeks, months, and even years to only days.

Gartner just lately named AI brokers one of many fastest-rising applied sciences on its 2025 Hype Cycle, but a current MIT research discovered that 95% of enterprise AI tasks fail to ship enterprise impression, not as a result of the fashions fall quick, however as a result of embedding them into workflows and information programs is simply too advanced. RavenDB’s AI Agent Creator removes that barrier by working brokers natively contained in the database, giving builders safe, direct entry to operational information. By retaining brokers near the information and automating integration, RavenDB turns months of uncertainty into days of dependable, context-aware AI supply.

“Consumers can already plan meals, design whole journeys, or get recommendation from AI platforms similar to ChatGPT and Gemini in seconds, but enterprises are nonetheless caught with inflexible, scripted chatbots,” stated Oren Eini, CEO and Founder of RavenDB. “Our AI Agent Creator is the lacking piece, enabling builders to construct totally built-in brokers straight throughout the database, with safe entry to stay operational information in order that they will go from concept to proof of idea in minutes, and production-ready brokers in a matter of just some days.”

Unlike scripted bots or AI-assisted chatbots restricted to generic data, with the AI Agent Creator, builders can deploy clever brokers with built-in guardrails. Developers outline the scope by which every agent can function on behalf of particular customers, whereas seamlessly connecting to present validation, authorization, and enterprise logic. To give builders stronger management, enhanced security, and larger precision over how information and operations are accessed, the massive language mannequin (LLM) follows a zero belief, default deny method, the place no information or operations are accessible except explicitly authorised.

When an end-user submits a request in pure language, RavenDB invokes the agent to course of the request and communicates the instruments and actions it has throughout the scope outlined by the developer. RavenDB then orchestrates the complete stream, referencing present enterprise logic to carry out authorised operations. This course of supplies correct, customized responses with out ever exposing the complete database, transferring information to exterior servers, writing advanced code, or compromising safety.

The function helps all LLMs and makes use of good caching, summarizing agent reminiscence and historical past, lowering redundant requests for reasoning-intensive duties. This considerably cuts AI spend and optimizes prices with out sacrificing accuracy, making it a essential effectivity instrument in agentic AI workflows.

Now obtainable as a part of RavenDB 7.1, the AI Agent Creator lets clients begin constructing and deploying brokers instantly, with out re-architecting programs or compromising information safety. RavenDB agentic AI runs throughout cloud, on-premise, and edge environments.

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