MongoDB(MDB)

The enterprise adoption of artificial intelligence has reached an inflection point. Organizations are rapidly moving into the era of agentic AI, autonomous systems capable of executing complex reasoning and making operational decisions independently. Yet as executives attempt to transition agents from sandbox environments into mission-critical production channels, they inevitably collide with an AI trust gap. Unlike traditional applications, agentic solutions interpret intent and take autonomous action on behalf of your business. Traditional IT tools are not designed to manage dynamic solutions. To scale securely, organizations must deploy a proactive control plane that evaluates an agent's logic and employs strict governance. In this article, we outline a four-step approach for building safety and optimization into agentic solutions. The approach outlines a broad framework that can be tailored to an organization’s specific needs. The 4-step framework for agentic trust To close the AI

There’s a common theme to the conversations I’ve been having with AI teams lately: change. Constant, head-spinning change. Teams across industries are evaluating and re-evaluating model providers, agent frameworks, and harnesses on a continuous basis. At MongoDB, we believe that your choice of technology partner—specifically, your data platform—should simplify how you build with AI. It should deliver performance at scale, enable you to build and run anywhere, and it should allow you to choose your own providers and frameworks. This is exactly what MongoDB offers, and it’s why more than 67,000 customers rely on us for their most important applications. The organizations seeing the most AI success are the ones whose technology stacks are set up for the current pace of change. For example, DevRev’s AgentOS platform is powered by MongoDB Atlas. AgentOS handles billions of requests each month, for everything from AI-assisted insights and analytics to internal communications and development.

Retail supply chains are not a back-office logistics function; they are a high-stakes, board-level concern. Imagine learning suddenly that shipment rerouting surcharges have doubled due to new regional escalations; the impact on competitive differentiation and consumer trust is immediate. As a result, a long-standing focus on linear efficiency and lean inventory is being disrupted by a mandate for resilience and AI-driven responsiveness. To survive, retailers must move beyond the rigidity of legacy systems and embrace an AI-ready data platform that can pivot as fast as headlines change. Indeed, a 2026 study by KPMG reported that businesses are establishing new performance metrics, centered around post-disruption recovery time, supplier diversification, sourcing agility, revenue growth from improved experiences, cost savings, and employee engagement. Now, retailers are modernizing their supplier management capabilities. An effective supplier management application that boosts visibility

Quarterly Report filed 2026-05-29

Material Event filed 2026-05-28

Proxy Statement filed 2026-05-19

As enterprise AI agent adoption scales, the absence of centralized, organization-level tool infrastructure is producing compounding costs. When adoption is built around optimizing for deployment speed, enterprises expose themselves to a combination of risks: duplicated engineering effort, security exposure, and operational opacity. Every enterprise needs its own shared tool registry, one that reflects its specific regulatory environment, security posture, and operational conventions. To be clear, this is not an argument for a public package manager, something like npm, PyPI, or Maven. The infrastructure each enterprise needs is internal; scoped to its own teams, its own data, its own policies, its own domain. Trying to expand the scope beyond the confines of individual organizations would be premature standardization in a fast-moving, nascent space. A shared enterprise tool registry is not an optimization or a nice-to-have. It is foundational infrastructure as agent deployments scale b

Today, we announced at .local London that MongoDB 8.3 is built for the speed AI demands—and our customers can't afford to wait. The data layer has to move at AI speed The old contract between databases and the applications on top of them was simple: databases improve slowly, and architectures evolve around them. AI has changed that contract. The workloads our customers are shipping today—agents retrieving at sub-100ms, retry storms hitting in milliseconds, multi-region deployments that can't trade compliance for latency—were edge cases 18 months ago. Now they're the baseline. MongoDB 8.3, generally available today, is our fourth significant release in 19 months. These releases compound. Customers running on 8.0 have seen 36% faster reads and 59% higher throughput for updates. 8.3 adds another 35% to write throughput, 45% to reads, and 15% to ACID transactions over 8.0 — without changing a line of application code. Enterprises like Adobe, running the most demanding AI in production, hav

This guest post comes from IDC’s Dr. William Lee, Senior Research Director, Service Provider and Core Infrastructure Research. MongoDB commissioned IDC to explore the connection between legacy infrastructure, data challenges, and AI across Asia Pacific, and today we’re happy to share that work. For more, see the full MongoDB-sponsored IDC InfoBrief, Modernizing Legacy: Winning in the Age of AI, Doc #AP242555-IB, April 2026. AI ambition is everywhere across Asia/Pacific. But ambition alone does not determine success. Organizations are discovering that AI outcomes are directly tied to the quality, accessibility, and modernity of their underlying technology stack and associated data technology foundations. Organizations that have managed to stay abreast of technical and data management changes across the application and infrastructure stacks, by embedding modernization into their organizational DNA, are experiencing 3x more digital revenue growth than those that are bound up in technical

You can often predict a load spike before it arrives. Maybe it happens at the same time every day, or there’s always a spike at midnight on a Friday when you run a certain batch job. Or maybe it’s not cyclical, but load is rising steadily, and it’s a reasonable guess that it will keep rising for a while. MongoDB Atlas’s reactive auto-scaler handles these spikes, but scaling to the right size takes several minutes. What if MongoDB Atlas could use these temporal patterns—cycles and trends—to scale up a replica set before it’s overloaded? In 2023, we prototyped predictive auto-scaling. We wanted to see if it was possible to predict rises and falls in load on MongoDB Atlas replica sets. We researched which machine learning models made the best predictions, and estimated how much a predictive auto-scaler could improve performance and save our customers money. MongoDB has now rolled out predictive auto-scaling. The production version of the algorithm is quite different from the prototype, an