MongoDB Delivers Slew of Updates to Make Enterprise AI Production Ready

MongoDB, Inc. is introducing new capabilities furthering its vision and strategy of delivering a unified AI data platform that gives enterprises everything they need to run agents in production.

"The hardest part of running agents in production isn't the model. It's the data layer underneath it," said CJ Desai, president and chief executive officer of MongoDB. "To trust an agent at scale, it has to retrieve the right context, hold memory across sessions, and operate at machine speed, wherever the enterprise needs it. That's why AI-native companies like ElevenLabs build voice agents on MongoDB, and why institutions like Lloyds Banking Group trust it for mission-critical workloads.”

With Automated Voyage AI Embeddings in MongoDB Vector Search, now in public preview, embeddings are now generated automatically as data is written or updated to give agents accurate, real-time context, according to the company.

Agents are only as good as what they remember and what they can retrieve. Embedding models convert information into vectors—an array of numbers that represent meaning mathematically—so an agent can find the right information.

The LangGraph.js Long-Term Memory Store, now generally available, gives JavaScript and TypeScript developers persistent, cross-conversation agent memory that Python developers have had—powered by MongoDB Atlas, as a single backend, with no additional database required.

"When AI tools and agents produce a wrong answer, the instinct is to blame the model," said Pablo Stern, chief product officer, AI, and emerging products at MongoDB. "But the data platform is what enables the agent with the right context and memory to act correctly. With MongoDB, we've made this easy. Developers no longer have to build and maintain data infrastructure, wire up embeddings, or manage syncing between systems. They can focus on business outcomes rather than the plumbing."

MongoDB 8.3, available now, delivers more reads, more writes, more ACID transactions, and more complex operations over MongoDB 8.0—without changing a line of application code, the company said.

“The requirements of enterprises running AI at scale are what we build for. MongoDB 8.3 makes agent workloads faster and cheaper to run on infrastructure customers already have. We've also moved common data transformations into the database itself, so teams no longer have to maintain external pipelines just to feed their agents. Production AI doesn't wait, and neither do we,” said Ben Cefalo, chief product officer, core products at MongoDB.

MongoDB runs across Amazon Web Services (AWS), Google Cloud, Microsoft Azure, on-premises, and in hybrid environments. Customers get one database, one API, and one set of skills that work consistently wherever they deploy, the company said.

Cross-region connectivity for AWS PrivateLink, now generally available, ensures that database traffic between MongoDB Atlas clusters in different AWS regions stays on the AWS private network, with no exposure to the public internet.

For more information about this news, visit www.mongodb.com.