MongoDB, Inc.MDB

MongoDB promotes its Atlas Agent Engine for agentic workloads and applications.

“I N V E S T O R D A Y Database Full Text Search Embeddings RerankersVector Search {JSON} Document Model Agent EngineATLAS AGENT ENGINE Memory & Context Secure governed Agentic Runtime Database Policy guardrails Three core investments that drive growth Run Anywhere & Enterprise Advanced Doubling down MongoDB 9.0 Enterprise proven real-time data Mission-critical elasticity Atlas Infinite”

MongoDB contrasts user-driven and agentic workloads by intensity and scale.

“I N V E S T O R D A Y User-Driven Workloads Agentic Workloads Bursty, unpredictable & intensePredictable cadence & intensity Intensity & scale means something different for Users vs. Agents”

MongoDB states Atlas Infinite suits the bursty nature of agentic applications.

“I N V E S T O R D A Y Perfect for the bursty nature of agentic applications Expands to new opportunities where compute & storage separation are critical Atlas Infinite Mission-critical elasticity Atlas Infinite will be a win for customers and drive growth for MongoDB”

MongoDB previews its open secure governed Atlas Agent Engine.

“I N V E S T O R D A Y PUBLIC PREVIEW ATLAS AGENT ENGINE READY FOR DESIGN Open, secure & governed agentic infrastructure”

MongoDB Atlas Agent Engine includes memory context governance and guardrails.

“I N V E S T O R D A Y MEMORY & CONTEXT-AWARE SECURE & GOVERNED OPEN & FLEXIBLE PROBLEM Enterprises won't deploy agents they can't trust to behave. WHAT WE DELIVER Agent Engine includes governance and guardrails that keep agent actions safe and compliant. PROBLEM Enterprises don’t want to bet on one model, framework, cloud, or environment. WHAT WE DELIVER Agent Engine is open, so enterprises don't have to re-architect their agents as the landscape changes. PROBLEM Agents that forget or misjudge context compound their mistakes. WHAT WE DELIVER Agent Engine provides Agent Memory so agents can learn and adapt over time. Atlas Agent Engine positions us to capture more of the agentic stack”

MongoDB outlines ecosystem levers to integrate with SI agentic stacks.

“I N V E S T O R D A Y FOUR LEVERS THAT POWER OUR ECOSYSTEM Data layer in SI agentic stacks 01 Co-innovate with hyperscalers and neo-clouds 02 Integrate across the AI ecosystem 03 04 Embed with industry ISVs”

MongoDB warns that use of generative and agentic AI in offerings or operations may cause reputational harm, liability and extra costs.

“Social, ethical, security and regulatory issues relating to the use of new and evolving technologies such as AI and ML, including generative and agentic AI and AI models, in our offerings, internal operations or partnerships, may result in reputational harm and liability, and may cause us to incur additional research and development costs to resolve such issues. The use of generative and agentic AI, relatively new and emerging technologies in the early stages of commercial use, exposes us to additional risks, such as damage to our reputation, competitive position, and business, legal and regulatory risks and additional costs. For example, generative AI has been known to produce false or “hallucinatory” inferences or output, and certain generative AI uses machine learning and predictive analytics, which can create inaccurate, incomplete, or misleading content, unintended biases, and other discriminatory or unexpected results, errors or inadequacies, any of which may not be easily detectable by us or any of our related service providers. The use of agentic AI, which is designed to perform tasks and make decisions with limited human intervention, introduces additional risks, including the potential for AI agents to operate unpredictably, exceed their prescribed authorizations, or fail to align with internal policies or legal obligations. As with many innovations, AI and ML presents risks and challenges that could affect its adoption, and therefore our business. Our offerings bas”

MongoDB is increasingly deploying generative and agentic AI tools internally for software development, security testing and operations.

“We are also increasingly deploying, and expect to continue to increasingly deploy, generative and agentic AI tools internally to support our own operations, including software development, security testing, and other business functions. The pace at which such tools are being introduced and adopted, both by us and more broadly across the technology industry, may outpace our ability to develop, implement and enforce corresponding security, access control and governance measures. Our employees, contractors and vendors may adopt or use such AI tools, including through unauthorized or unsanctioned means, before we have had the opportunity to assess and mitigate the associated risks, which could result in the exposure of our or our customers' confidential, proprietary or personal information, interactions with unauthorized data sources or external tools, non-compliance with our internal security policies or applicable law, or other operational failures that may be difficult to detect or remediate through our traditional oversight mechanisms. Any of the foregoing could harm our reputation and adversely affect our business, results of operations and financial condition.”

MongoDB warns that content created using generative or agentic AI tools may lack copyright protection and create liability risks.

“Moreover, any content created by us using generative or agentic AI tools may not be subject to copyright protection which may adversely affect our intellectual property rights in, or ability to commercialize or use, any such content. In the United States, a number of civil lawsuits have been initiated related to the foregoing and other concerns, the outcome of any one of which may, amongst other things, require us to limit the ways in which we use AI in our business. For example, the output produced by generative AI tools may include information subject to certain rights of publicity or privacy laws or constitute an unauthorized derivative work of the copyrighted material used in training the underlying AI model, any of which could also create a risk of liability for us, or adversely affect our business or operations. Our business, financial condition and results of operations may be adversely affected if content, recommendations, solutions or features or their development or deployment (including the collection, use, or other processing of data used to train or create such AI and ML solutions or features) are found to have or alleged to have infringed upon or misappropriated third-party intellectual property rights or violate applicable laws, regulations, or other actual or asserted legal obligations to which we are or may become subject.”

Rapid adoption of generative and agentic AI tools may change developer workflows and reduce demand for MongoDB's platform.

“In addition, the rapid adoption of generative and agentic AI tools may change how software developers design, build and deploy applications, including through increased automation of coding, infrastructure configuration and application architecture. Because a significant portion of our strategy is focused on being the database platform of choice for developers, changes in developer workflows, tooling or decision-making processes resulting from the adoption of AI-enabled development tools could affect how developers select databases or other data platforms. If these evolving development patterns reduce developer engagement with, or preference for, our platform or favor alternative technologies or architectures, it could adversely affect demand for our products and services.”

MongoDB cites risks from use of generative and agentic AI in offerings and operations.

“Social, ethical, security and regulatory issues relating to the use of new and evolving technologies such as AI and ML, including generative AI and AI models, in our offerings, internal operations or partnerships, may result in reputational harm and liability, and may cause us to incur additional research and development costs to resolve such issues. The use of generative and agentic AI, relatively new and emerging technologies in the early stages of commercial use, exposes us to additional risks, such as damage to our reputation, competitive position, and business, legal and regulatory risks and additional costs. For example, generative AI has been known to produce false or “hallucinatory” inferences or output, and certain generative AI uses machine learning and predictive analytics, which can create inaccurate, incomplete, or misleading content, unintended biases, and other discriminatory or unexpected results, errors or inadequacies, any of which may not be easily detectable by us or any of our related service providers. As with many innovations, AI and ML presents risks and challenges that could affect its adoption, and therefore our business. Our offerings based on AI and ML may expose us to additional claims, demands and proceedings by private parties and regulatory authorities and subject us to legal liability as well as brand and reputational harm.”

MongoDB warns of risks from rapid adoption of generative and agentic AI tools.

“In addition, the rapid adoption of generative and agentic AI tools may change how software developers design, build and deploy applications, including through increased automation of coding, infrastructure configuration and application architecture. Because a significant portion of our strategy is focused on being the database platform of choice for developers, changes in developer workflows, tooling or decision-making processes resulting from the adoption of AI-enabled development tools could affect how developers select databases or other data platforms. If these evolving development patterns reduce developer engagement with, or preference for, our platform or favor alternative technologies or architectures, it could adversely affect demand for our products and services.”

MongoDB announced new platform capabilities for AI agent memory and production deployment.

“• At MongoDB.local London, MongoDB announced seven new platform capabilities designed to close the gap between AI experimentation and high-performance production deployment. These updates advance MongoDB’s automated retrieval and persistent AI agent memory capability, strengthen the core database foundation for mission critical workloads, and help upskill builders to deploy production AI.”

MongoDB and LangChain partnered to make Atlas a backend for production AI agents.

“• MongoDB and LangChain formalized a strategic partnership to transform MongoDB Atlas into the premier unified backend for production-ready AI agents. This collaboration streamlines the transition from prototype to scale by integrating vector search, persistent memory, and natural-language querying into a single, enterprise-grade platform.”

MongoDB warns that use of generative and agentic AI in offerings or operations may cause reputational harm and liability.

“Social, ethical, security and regulatory issues relating to the use of new and evolving technologies such as AI and ML, including generative AI and AI models, in our offerings, internal operations or partnerships, may result in reputational harm and liability, and may cause us to incur additional research and development costs to resolve such issues. The use of generative and agentic AI, relatively new and emerging technologies in the early stages of commercial use, exposes us to additional risks, such as damage to our reputation, competitive position, and business, legal and regulatory risks and additional costs. For example, generative AI has been known to produce false or “hallucinatory” inferences or output, and certain generative AI uses machine learning and predictive analytics, which can create inaccurate, incomplete, or misleading content, unintended biases, and other discriminatory or unexpected results, errors or inadequacies, any of which may not be easily detectable by us or any of our related service providers. As with many innovations, AI and ML presents risks and challenges that could affect its adoption, and therefore our business. Our offerings based on AI and ML may expose us to additional claims, demands and proceedings by private parties and regulatory authorities and subject us to legal liability as well as brand and reputational harm.”

MongoDB states rapid adoption of generative and agentic AI tools may alter developer workflows and reduce demand for its platform.

“In addition, the rapid adoption of generative and agentic AI tools may change how software developers design, build and deploy applications, including through increased automation of coding, infrastructure configuration and application architecture. Because a significant portion of our strategy is focused on being the database platform of choice for developers, changes in developer workflows, tooling or decision-making processes resulting from the adoption of AI-enabled development tools could affect how developers select databases or other data platforms. If these evolving development patterns reduce developer engagement with, or preference for, our platform or favor alternative technologies or architectures, it could adversely affect demand for our products and services.”

MongoDB offers an integrated platform with search, vector search, and embeddings for AI agents in a single data layer.

“"Whether it's AI & digital natives looking for a highly performant solution that dynamically scales, a large enterprise looking for multi-cloud resiliency for their mission critical applications, or a customer seeking an integrated offering for AI agents with features such as search, vector search and embeddings in a single intelligent data layer, customers are excited about the strength of the MongoDB platform, the innovations we have been bringing to market, and plan to deliver in the years to come."”

MongoDB launched its MCP Server in public preview to connect deployments to AI agent tools like Cursor and Claude.

“• The MongoDB Model Context Protocol (MCP) Server was launched in public preview, which connects MongoDB deployments to MCP-supported clients like Windsurf, Cursor, GitHub Copilot in Visual Studio Code, and Anthropic’s Claude, making it easy for developers to interact with data using natural language and perform database operations with their favorite agentic AI tools, assistants, and platforms.”