I gave the following talk at Microsoft Ignite 2025:
Connecting LLMs to your secure, operational database involves complexity, security risks, and hallucinations. This session shows how to build context-aware AI agents directly on your existing data, going from live database to production-ready, secure AI agent in hours. You'll see how to ship personalized experiences that will define the next generation of software. RavenDB's CEO will demonstrate this approach.
Want to see how modern applications handle complexity, scale, and cutting-edge features without becoming unmanageable? In this deep-dive webinar, we move From CRUD to AI Agents, showcasing how RavenDB, a high-performance document database, simplifies the development of a complex Property Management application.
AI agents are only as powerful as their connection to data. In this session, Oren Eini, CEO and Co-Founder of RavenDB, demonstrates why the best place for AI agents to live is inside your database. Moderated by Ariel, Director of Product Marketing at RavenDB, the webinar explores how to eliminate orchestration complexity, keep agents safe, and unlock production-ready AI with minimal code.
You’ll see how RavenDB integrates embeddings and vector search directly into the database, runs generative AI tasks such as translation and summarization on your documents, and defines AI agents that can query and act on your data safely. Learn how to scope access, prevent hallucinations, and use AI agents to handle HR queries, payroll checks, and issue escalations.
Discover how RavenDB supports any LLM provider (OpenAI, DeepSeek, Ollama, and more), works seamlessly on the edge or in the cloud, and gives developers a fast path from prototype to production without a tangle of external services. This session shows how to move beyond chatbots into real, action-driven agents that are reliable, predictable, and simple to extend. If you’re exploring AI-driven applications, this is where to start.
Unlock practical AI agents inside your database. In this live demo and deep dive, Oren Eini shows how to build real, production-ready AI agents directly in RavenDB that query your data, take actions, remember context, and stay inside strict security guardrails. You will see an agent defined in a few lines of code, connected to OpenAI or any LLM you choose, running vector search and RAG over your catalog, and safely executing business actions like “add to cart,” “find policies,” or “sign document,” all with parameters that are enforced by the database rather than trusted to the model. You will learn how RavenDB agents eliminate fragile glue code by giving the model explicit tools: data queries that return typed results and server-side actions you validate in your code.
Conversations are stored as documents, with automatic token-aware summarization to control latency and cost. The demo streams responses token by token for responsive UX, switches models without rewrites, and shows how scope parameters prevent data leaks even if the prompt is manipulated. You will also see a multi-tool HR assistant that chains tools, coordinates front end and back end, and persists state. The session closes with a look at the roadmap, including multi-agent orchestration and AI assist inside Studio.
Yesterday I gave a live talk about some of the re-design we did to the internals of RavenDB’s storage engine (Voron). I think it went pretty well, and the record is here.
Last week I did an hour long webinar showing AI integration in RavenDB. From vector search to RAG, from embedding generation to Gen AI inside of the database engine.
Most of those features are already released, but I would really love your feedback on the Gen AI integration story (starts at around to 30 minutes mark in the video).
Watch Oren Eini, CEO of RavenDB, as he delves into the intricate process of constructing a database engine using C# and .NET. Uncover the unique features that make C# a robust system language for high-end system development. Learn how C# provides direct memory access and fine-grained control, enabling developers to seamlessly blend high-level concepts with intimate control over system operations within a single project. Embark on the journey of leveraging the power of C# and .NET to craft a potent and efficient database engine, unlocking new possibilities in system development.
I’m going deep into some of the cool stuff that you can do with C# and low level programming.
When Oren Eini originally developed RavenDB, he used the Lucene library to implement indexing. Eventually, his team encountered limitations with this strategy, so they created the Corax search engine, which improved query execution time significantly. Oren discusses the challenges involved in creating this engine and the approaches they took to overcome these challenges.
In this episode of The Modern .NET Show podcast, Oren Eini, a seasoned developer with over 20 years of experience in the .NET field, discussed the evolution of the .NET framework and the complexities that come with it. Eini highlighted the rapid pace of change in the language, from the introduction of generics at version 2.0 to switch expressions and pattern matching in the latest versions. While these new features allow for more concise code, Eini acknowledged that they also increase the scope and complexity of learning C# from scratch.
What is data sharding, and why do you need it? Carl and Richard talk to Oren Eini about his latest work on RavenDB, including the new data sharding feature. Oren talks about the power of sharding a database across multiple servers to improve performance on massive data sets. While a sharded database is typically in a single data center, it is possible to distribute the shards across multiple locations. The conversation explores the advantages and disadvantages of the different approaches, including that you might not need it today, but it's great to know it's there when you do!
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