Showing category results for AI

Over a typical 2 day window, we record 10 times as many emulator sessions lasting more than 30 minutes for vNext as for the legacy emulator. Our bet is that agentic development is helping drive the difference! The appeal is straightforward: coding agents need a database they can run anywhere. They also need a database they can start, seed, test ag...

The first few minutes of a new AI project often feel productive. Create a chat endpoint, connect a model, send a prompt, and display the answer. Then the real questions arrive. Where should the agent store memory? How do we keep one customer's data away from another? How do we trace the source of an answer? What happens when an action require...

You can now open Azure Cosmos DB Shell directly from Data Explorer in the Azure portal. The shell gives you a terminal-style way to work with Azure Cosmos DB. You can move through databases and containers as you would through folders, run NoSQL queries, inspect resources, and update data. When you launch it from Data Explorer, it is already conn...

Anthropic's 2026 Agentic Coding Trends Report captures an important tension in how developers use AI. Engineers report using AI in roughly 60% of their work, yet say they can fully delegate only 0–20% of their tasks. AI is becoming a constant collaborator, but it still needs setup, supervision, validation, and human judgment. That gap is especia...

Recently we introduced the Agent Memory Toolkit and the Agentic Retrieval Toolkit for Azure Cosmos DB. The Agent Memory Toolkit gives your agents durable, Cosmos-backed memory: it stores raw conversation turns and then distills them into higher-value derived memories (thread summaries, extracted facts, and cross-thread user profiles), all searchabl...

If you use the Azure Cosmos DB vNext emulator, you probably know the local development loop: start the emulator, connect to it, create some resources, load test data, run queries, and inspect the results. Each step is straightforward, but together they add setup work before you can test the application you are actually building. How agents work wi...

Most "AI agent" demos forget everything the moment the process exits. That's fine for a toy project, but useless for anything real. An agent that helps you write, triage, or support needs two things a language model alone can't give it: durable state and the ability to recall the right context by meaning. This post shows how to build exactly that b...

The wait is over. Spring AI 2.0 is generally available, and Azure Cosmos DB is right there with it. With this release, Spring AI graduates into a mature, production-ready framework for building AI applications in Java, and Azure Cosmos DB ships dedicated, vendor-maintained integrations that plug straight into the Spring AI ecosystem. The Spring AI...

AI applications built on Azure Cosmos DB depend on embeddings for grounded results. Keeping them in sync with your data is the hard part: it means building and operating a separate data pipeline to track changes, call an embedding model, and write the results back to Azure Cosmos DB. In practice, that pipeline also has to handle failures and retrie...

Today we are open-sourcing OmniVec, a platform for building and operating the embedding pipelines that keep the vector representation of your operational data in sync as it changes. You register data sources, embedding model(s), vector stores (destination), and OmniVec does the rest: initial backfill, change tracking, model invocation to geenrate, ...