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10 Years of MongoDB Atlas: Built for What’s Next
June 25, 2026
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Closing the Gap Between Agentic Promise and Impact with Industry Leading Retrieval
A recent McKinsey report* noted that AI agents and robots “could generate about $2.9 trillion in US economic value per year,” while another study from WRITER** quantified the speed at which organizations have moved toward agents: 97% of C-suite executives surveyed indicate their companies have deployed AI agents, and 75% expect agents to become members of the C-suite in the next five years.
MongoDB for the Agentic Era: Built for Developers and AI Agents
How developers work has dramatically changed. Anthropic's 2026 Agentic Coding Trends Report* found that developers now use AI in roughly 60% of their engineering work. Coding agents are no longer just executing decisions; they're helping make them. If you’re building software today, it has to work for both humans and the agents working alongside them.
How Greater China Organizations Power AI And Compliance At Scale
Organizations across Greater China are heavily investing in data-driven technologies to deliver innovative digital services and experiences. According to Morgan Stanley Research, AI investments in China alone could deliver a 52% return on invested capital by 2030*.
MongoDB Atlas Delivers Greater Capacity, Reliability, and Operational Confidence
Production reliability is rarely lost in a single dramatic moment. More often, it is challenged by the realities operators face every day: cloud capacity constraints that delay critical scaling operations or the need to recover a single collection without disrupting an entire environment.
Data Sovereignty Is a Spectrum. Your Strategy Should Be Too.
When organizations talk about “data sovereignty,” they are usually expressing concerns about how concrete legal and regulatory regimes apply to their workloads—from data protection laws and sectoral localization rules to requirements around exit strategies and third‑party access.
Atlas Stream Processing Brings Operational Data to Apache Iceberg
Every order placed, message sent, or sensor reading captured lands in an operational database. Analysts, data scientists, and AI teams all want that data, because the freshest signal about a business lives in the systems running it. But getting this data from the application layer to the lakehouse, continuously and at scale, has been one of the most stubborn problems in the modern data stack.
Improving Agent Retrieval with Native Reranking and Hybrid Search
In production AI, what the system retrieves shapes everything that follows. It determines whether an application surfaces the right context in the first place and how much irrelevant information gets passed to an LLM. That affects two things you care about most: answer quality and cost.
MongoDB Search and Vector Search Now Run Anywhere
MongoDB is excited to announce the general availability of MongoDB Search (full-text) and Vector Search for MongoDB Enterprise Advanced and MongoDB Community Edition. Since announcing the public preview at MongoDB.local New York last year, these milestones represent MongoDB’s commitment to ensuring developers can build intelligent, search-driven applications wherever their data lives without compromising on capability, data control, or infrastructure flexibility.
Retrieval Accuracy Is Now a Competitive Advantage
The token economy is here. And as soon as you have an economy, people start budgeting. So it’s no surprise that most conversations about AI now come back to the token budget. Teams are being asked to show the value of inference—the action phase of AI—spend and justify the cost. The answer traces back to the data that was retrieved and fed to the LLM. This step is intuitively referred to as “retrieval.”