MongoDB Builder Blog
Deep dives into technical concepts, architectures, and innovations with MongoDB.
How We Used AI to Bring MongoDB to DynamoDB
August 24, 2026
Builder Blog
How to Encrypt LangGraph Checkpoints With MongoDB CSFLE
Would you trust an AI agent if a database administrator could read every message in its session? For a customer-facing chatbot, that reality already feels unsettling. Even if you trust your internal operators, plaintext checkpoints remain equally readable to anyone who gains database access during a breach.
Code Mode Without the Interpreter: Agents That Write Aggregation Pipelines
Standfirst Code mode gives an AI agent a code interpreter and lets it write real programs—one round trip instead of five, filtering where the data lives instead of in the context window. It also means running arbitrary code that a model wrote, which is a hard sell in a regulated product. This is an account of doing the same thing without the interpreter: the agent authors a MongoDB aggregation pipeline, a handler executes it server-side, and only the reshaped result crosses back. Two production systems, the query work that made them viable, and where the approach falls down.
Data Modeling and Schema Design for MongoDB Search Part 2: Use Cases and Patterns
This is Part 2, continuing from “Part 1: Practical Considerations.” In Part 1, the foundational concerns were laid out. Here in Part 2, common use cases and patterns to address them with best practices are covered.
Analyze MongoDB Atlas Data in OpenAI ChatGPT Work
MongoDB Atlas is now a connected data source in OpenAI's Data Plugin, powered by the MongoDB Atlas Managed MCP Server. Analysts and business teams can point the Data agent in ChatGPT Work at the data in their Atlas clusters and ask what changed in the moment, why it changed, and what to look at next, then turn the answer into a report or real-time dashboard without leaving the conversation.
Build a Fraud Review Agent with MongoDB Atlas and Claude Managed Agents
Claude Managed Agents lets you run Anthropic's Claude as a fully hosted agent, without standing up your own inference or orchestration infrastructure. Pairing Claude Managed Agents with MongoDB Atlas gives your agents fast, secure access to operational and vector data right where it lives, so you can ship agentic workflows without gluing together separate retrieval and hosting layers.
MongoDB VFS for LangChain Deep Agents: A Searchable Filesystem for Agents
Modern AI agents are no longer expected to answer a question alone. They're expected to complete real work: plan a multi-step task, inspect source material, produce intermediate artifacts, and hand off pieces of the problem to other agents, compressing work that used to take days into hours. That shift raises the bar on what an agent needs underneath it. An agent can only plan and execute at that speed if it can reliably navigate, search, read, and update the filesystem it's working with along the way.
MongoDB Atlas Joins Google Cloud Foundation Fabric (FAST): Secure Private Data Infrastructure in Minutes
Transitioning from on-premises infrastructure to the cloud can be challenging. Many organizations lack a clear understanding of cloud fundamentals, and establishing a foundational environment is often complex and time-consuming. For every enterprise in particular, security is a critical priority and must be robustly implemented from the outset. Additionally, for new teams building applications, the initial setup presents significant difficulties, as they may not have clear guidance on the precise steps required to create a secure and reliable cloud environment.
Agent Observability: Monitoring Decisions, Not Requests
Observability is the practice of making a system’s internal behavior legible from the outside—capturing enough signal that an engineer can reconstruct what happened and why. For a web service, that signal is the request: a status code, a latency number, a stack trace when something throws. For an agent, the unit of behavior is a decision: the choice it makes between one call and the next. The agent reads context, chooses an action, calls a tool or a model, observes the result, and chooses again.
How We Used AI to Bring MongoDB to DynamoDB
Amazon has a history of favoring customer choice, and ExtendDB is another signal of that: an open source, pluggable adapter built specifically so DynamoDB workloads can run in more environments. At MongoDB, we also believe that everything starts with serving the customer well, so we work backwards from there. This is why we built a MongoDB storage backend for ExtendDB, AWS's open source DynamoDB-compatible adapter, which is ready for use today.
Building E-commerce AI Agents on MongoDB with CrewAI
AI-powered shopping agents are becoming a real differentiator for e-commerce teams. A 2026 Stord report on AI in e-commerce revealed a 34% year-over-year increase in the number of consumers who reported using generative AI for online shopping. The trend is even more pronounced among younger consumers: 37% of Gen Z and 29% of Millennials actively use AI to shop online. After seeing how the agentic commerce journey actually works, it's not hard to understand why.