The industry has not yet converged on a shared vocabulary for this infrastructure. ” That is the problem this series addresses. McKinsey’s 2025 State of AI report3 found 23% of enterprises self-report scaling agents, and roughly 5% in full production. This series is a guide to that infrastructure. The infrastructure around the model—memory, state, orchestration, observability, evals, security, and the data layer that feeds all of it—determines whether anything actually works. The LLM is the smallest part of a production agent system.
Open source database software from experts who stand with you in production. Migrations are designed to minimize disruption and preserve application compatibility. It supports Day 1 and Day 2 operations including deployment, spanian casino scaling, upgrades, backups, restores, and high availability for production MongoDB clusters. Unlike MongoDB Atlas, Percona avoids consumption-based pricing, cloud restrictions, and proprietary operational dependencies Percona for MongoDB gives teams control over where and how MongoDB runs—on-premises, in the cloud, hybrid, or on Kubernetes—without being tied to a managed cloud platform. Percona software enhances MongoDB Community Edition with enterprise capabilities while preserving full deployment flexibility. Clear, direct answers about our software, support, licensing, and how things actually work in production.
Gartner predicts1 40% of enterprise applications will embed AI agents by the end of 2026, and 40% of agentic AI projects2 will be cancelled by the end of 2027. Production agent systems share more in common with production ML systems than most teams realize. Discover resources for catalog use cases in retail, financial services, and additional industries and sectors. Enrich the payments experience by driving value-added services and features to consumers. Quick and accessible resources to help build applications on MongoDB. MongoDB’s trusted full-text and vector search capabilities are now available as an add-on to Enterprise Advanced subscriptions. Before building a skincare routine, identify your skin type to discover the sorts of skincare products that may work for you.
A platform missing any of these produces harnesses that appear to work in controlled conditions and fail under production load. A platform is the infrastructure that runs many harnesses across many teams over time—durable execution, fleet-wide governance, cross-harness identity, lifecycle management. Each post in this series covers one component of the harness, or of the infrastructure underneath it. MongoDB Enterprise Advanced is the most flexible way to run the modern database for production applications. This is a strong foundation for your new skincare routine and can be added to as you learn more about your skin's needs. In your Google Account, you can find directions from your home or work quicker when you set your home and work addresses.
Handle and blend IoT and operational data in real-time. Maintain your core systems at lower cost and risk. Support for data definition language (DDL) operations such as creating and dropping indexes and collections so you can react to database events in addition to data changes To make sure that your application works the way you expect, you must evaluate it. This is because this additional information can overload the model's context to make it 'forget' when to call the internal tools. You can prompt to give the model additional context about how to use custom tools. They can also add latency if the model is calling the custom tools before responding to a user. This guide covers general context engineering best practices in addition to some specific aspects for the MongoDB Responses API.
