Presented by MongoDB. The term “architectural drag” describes the mismatch between what AI models and agents can generate and what legacy infrastructure can reliably support. The data layer beneath an agentic system must simultaneously handle variable schemas, vector embeddings, real-time retrieval, and multi-tenant scale—often without human-managed migrations. Traditional relational databases were not designed for document flexibility or AI-native workloads.
Fixed schemas force manual updates whenever agents introduce new data shapes, and separate vector stores add latency and synchronization overhead. Three digital-native startups—Modelence, Tavily, and Huntr—tackled these challenges by building on MongoDB Atlas, which unifies document storage, native vector search, hybrid search, and managed autoscaling.
Modelence: building an agent-native cloud
Modelence provides an open-source framework and AI app builder aimed at agent-native development, allowing users to build and deploy production-ready web apps, APIs, and databases in minutes. The company identified early that most backend infrastructure was designed for humans rather than AI, and that rigid schema management and complex migrations create operational drag that causes agent workflows to fail in production.
Aram Shatakhtsyan, co-founder and CEO of Modelence, said: “Choosing MongoDB helped us keep everything in a single place, which is an important property of what we strive to do for our own users. Live data streams, vector search, all as part of the main database. For AI agents, it’s especially important to have a single platform where everything can be done, because connecting multiple platforms together makes it more error prone.”
Modelence standardized on MongoDB Atlas because the document model matches how AI agents process and generate data: schemas can evolve quickly without manual migrations. The team layers a typed schema on top of that flexibility to improve the accuracy of generated, production-ready apps. TypeScript integration has been especially consequential: MongoDB types and values translate directly to TypeScript, making the database a single source of truth for both app logic and data in the Modelence App Builder.
The platform can move from planning to a running feature in minutes with fewer regressions. That speed and reliability helped Modelence raise $3 million in seed funding and launch an AI-native app builder that covers the full application lifecycle.
Tavily: the web access layer for agents
Tavily provides a search API built for AI agents, connecting them to real-time web knowledge so they stay grounded in current information rather than static training data. At Tavily’s scale, each agent request must authenticate, retrieve, and be metered without friction, which required backend infrastructure that absorbs change without breaking.
Tomer Weiss, Data Team Lead at Tavily, said: “On the user side, every agent request authenticates and meters against it. On the data side, we use it to track the lifecycle of every document we’ve ever touched: when it was fetched, how stale it is, what the freshness signals were and how popular it is. MongoDB’s flexible schema let us keep evolving those records without migrations as new metrics and features came along.”
Tavily’s multi-tenant requirements include managing millions of API keys, distinct usage profiles, plan tiers, and regional residency rules. The company separated concerns across clusters early: a user/account cluster optimized for low-latency authentication and usage writes, and a sharded cluster for document state where the scaling axis is URLs rather than users. According to Weiss, the most important lesson was to pick infrastructure that doesn’t punish change—this flexibility provided more value than any single feature.
Huntr: from job tracker to AI career platform
Huntr.co is an AI resume-building and tailoring platform serving more than 500,000 job seekers across 190 countries. For a lean three-person engineering team, the challenge was to find a data foundation flexible enough to represent the full complexity of a person’s career history in a form that AI can read, reason about, and generate from.
Trevor McCann, senior software engineer at Huntr, said: “The kinds of career data we are gathering at Huntr naturally aligns with MongoDB’s document model. The core problem we’re solving with AI job search tools is how to surface the qualities of a candidate that make them unique. We need to be ready to store whatever kinds of data the candidate wants to include in their materials.”
Huntr built its AI Resume Builder on MongoDB Atlas because the document model mirrors career data: deeply nested, variable across candidates, and constantly evolving as the product ships features. MongoDB Search on Atlas covers core search needs while MongoDB Vector Search powers the Job Tailoring feature, which compares a candidate’s stored profile with a specific job description and uses semantic matching to generate an optimized resume.
The integrated database, search, and vector capabilities have directly accelerated development. McCann notes that hybrid search allows seamless queries across literal and semantic matches, a necessity for diverse data. The team considers MongoDB effectively a fourth member of their engineering team. Huntr is building toward AI that learns from a candidate’s full professional history over time to deliver increasingly personalized guidance.
The digital-native blueprint
The three startups’ experiences outline a digital-native blueprint for the agentic era based on three pillars:
- Unified platform: combining database, search, and vector storage into a single layer removes the integration and migration tax that slows development.
- Infrastructure that tolerates change: rapid shifts in AI workloads make flexibility more valuable than any single feature.
- Multi-tenant, scalable design: planning for millions of API keys, regional rules, and usage tiers must be intrinsic to the architecture.
Winners in the AI era will be those who build the most performant, durable, and flexible systems to support models in production. As agentic workflows become more sophisticated, the data foundation will determine how quickly teams can ship, how reliably agents operate, and how fast platforms can adapt to new requirements.
Note on content
This article is presented by MongoDB, indicating a sponsored or business relationship behind the content.
