Open-source document database platform RavenDB has launched what it calls “the first fully integrated database-native AI Agent Creator,” a tool that makes it easier for enterprises to build and deploy AI agents.
The platform tackles a common problem in enterprise AI – the difficulty of connecting models to a company’s own data systems and workflows securely and cost-effectively.
Making AI practical, not just powerful
The company wants to make AI deployment faster and more secure. Oren Eini, CEO and Founder of RavenDB, said the goal is to make AI deliver real value by embedding it directly where company data already lives. He explained that many organisations struggle because their data is scattered in multiple systems and formats, making integration expensive and complex.
“The biggest problem users have with building AI solutions is that a generic model doesn’t actually do anything valuable,” he said. “For AI to bring real value into your system, you need to incorporate your own systems, data, and operations.”
RavenDB’s new AI Agent Creator eliminates much of the overhead by letting companies expose relevant data to a model directly in the database – without separate vector stores or ETL workflows. The system manages technical challenges automatically, like model memory handling, summarisation, and data security.
According to Eini, this means companies “can move from an idea to a deployed agent in a day or two.”
Direct data access and real-time answers
Traditional AI workflows usually involve exporting data from a database to a vector store, then connecting that store to an AI model, creating delays and security gaps. RavenDB’s approach uses built-in vector indexing and semantic search to make information available instantly to AI agents inside the database itself.
That design supports real-time responsiveness, letting an AI agent access newly-updated information immediately: For example, checking a customer’s latest order or shipment status without waiting for a data refresh.
On the question of security, Eini said: “An AI agent will not be executed as a privileged part of the system,” he noted. “It functions as an external entity with the same access rights as the user operating it.”
Use cases and industry insight
Eini noted that RavenDB has already applied the AI Agent Creator in real customer environments. In one example, the system is used for candidate ranking in recruitment, automatically reading and comparing uploaded resumés against job requirements to identify promising applicants. In another example, Eini explained how AI Agent Creator is being used to re-rank semantic search results to output accurate relevance rather than just find the nearest vector matches.
Industry analysts see this kind of integration as part of a larger shift toward embedded, domain-specific AI. In a recent Forrester report, senior analyst Stephanie Liu wrote, “AI agents are eyeing autonomy, but your poor documentation means they may not reach this threshold.”
She said that while full autonomy remains challenging, tighter links between AI systems and live enterprise data can “deliver immediate, practical value” for organisations experimenting with agentic AI.
Broader context
Database-native AI could mark a big shift in how companies use machine intelligence in their operations. By keeping both compute and security barriers inside the database, platforms like RavenDB could cut down on the need for additional infrastructure layers – a challenge many businesses face as they scale their AI programmes.
AI News recently covered Google’s Gemini Enterprise, which aims to bring AI agents into everyday business workflows, and examined how CrateDB is rethinking database infrastructure for real-time AI performance. These are two major developments that reflect how agentic systems and data-centric architectures converge to make enterprise AI more efficient.
RavenDB’s latest addition builds on that trend, positioning databases as active participants in AI pipelines, not passive data dumps.
Looking ahead
Eini said the launch reflects RavenDB’s roadmap to make AI capabilities a native part of its platform. Over the past year, the company has added vector search, embedding generation, and generative AI features directly into the database engine.
“We aim to encapsulate all the AI complexity inside RavenDB,” he said, “so users can focus on the results rather than the mechanics.”
As enterprises continue to seek reliable, cost-efficient ways to adopt AI, database-native tools like RavenDB’s AI Agent Creator may offer a practical path forward, merging operational data and intelligence in one environment.
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