How to move agents from prototype to production with MongoDB + LangChain
Production agents need durable state, retrieval, structured querying, and observability. MongoDB + LangChain consolidates all that into one cluster.
Blog
The collection nobody documented: meeting a legacy MongoDB with an AI agent
MongoDB’s flexibility is a feature. It’s also how data drifts for years without anyone noticing. 3T MCP gives an AI agent the right instruments to walk into that collection and hand you back a map, schema reality, PII reality, and query reality, in the time it takes to ask.
Database migration for AI readiness: A practical guide
Traditional databases weren’t built for AI workloads. Rigid schemas, limited vector support, and expensive vertical scaling are slowing teams down. Here’s how to migrate to an AI-ready database, without the downtime, data loss, or spiraling costs.
Is your data putting AI projects at risk?
AI projects are failing because the data feeding the models can’t be trusted. Here’s what ungoverned data actually costs you, and what better workflows and data governance look like.
Your MongoDB data, now one prompt away
Studio 3T 2026.9.0 introduces a more powerful AI Helper and local MCP server for MongoDB. So you can inspect, query, and understand your MongoDB data in plain English, without writing a single line of code.
The real reason your MongoDB queries are slow (and how to diagnose them like a pro)
Understand how indexes can fix query slowness issues and give you blazing-fast MongoDB performance.
Make MongoDB easier with our Studio 3T demos
Make MongoDB easier with our new Studio 3T demo video series. See how to simplify queries, build aggregation pipelines visually, and use AI to work faster with your data.
What is ACID compliance in databases? A modern guide to transactional guarantees
Discover how multi-document transactions enable ACID Compliance across schema-flexible NoSQL architectures.
AI for DevOps: Why organizations must rethink roles, not just automate tasks
AI is transforming how software is built, tested, and deployed. In this DBTA webinar recap, we cover the key takeaways from our CEO Peter Caron and other industry experts on how AI is reshaping engineering roles, workflows, and data reliability in modern DevOps environments.
Why engineering leaders must fix data readiness before scaling AI
Scaling AI successfully depends on more than powerful models. Without strong data readiness for AI, engineering teams struggle with inconsistent data, hidden schema issues, and unreliable pipelines. Before organizations invest further in AI development, leaders must ensure their data is accessible, structured, and trustworthy enough to support it.
