Production-Ready Agents Need A Production-Ready Data Platform

Summary

AI development teams face constant shifts in model providers and agent frameworks, demanding data platforms that provide scalable, real-time context management. Agentic workloads require blending unstructured enterprise data, short-term session state, and persistent long-term memory. MongoDB addresses these requirements through its native JSON document model and integrated retrieval capabilities, combining full-text search, vector search, and hybrid search directly over operational data. Customer implementations such as DevRev, ElevenLabs, and Adobe rely on Atlas to achieve sub-100 millisecond hybrid retrieval and handle billions of requests. Additionally, MongoDB is collaborating with LangChain and ecosystem partners to establish open reference architectures and shared interfaces for portable agent memory across frameworks.

Context

AI development teams face rapid shifts in model providers, harnesses, and agent frameworks while building agentic systems that require real-time context combining short-term memory, persistent long-term knowledge, and unstructured enterprise data without operational fragmentation or stale data.

Approach / What changed

MongoDB leverages its native JSON document model for dynamic metadata and schema flexibility, integrating vector search, full-text search, and hybrid retrieval directly into its operational database. Additionally, MongoDB is partnering with LangChain to develop an open reference architecture for portable agent memory.

Takeaways

• MongoDB integrates full-text search, vector search, embeddings, and hybrid retrieval directly on its operational OLTP engine, eliminating separate data sync pipelines.
• Adobe implements MongoDB Atlas Search and Atlas Vector Search to execute sub-100 millisecond hybrid search queries for its Journey Agent.
• MongoDB and LangChain are collaborating on an open reference architecture to create shared interfaces, metadata conventions, and retrieval semantics for portable agent memory.

Related reading

• MongoDB · Sep 23, 2025

  Build AI Agents Worth Keeping: The Canvas Framework

  Enterprise AI agent initiatives frequently stall after pilot phases due to technology-first thinking, governance gaps, infrastructure complexity, and poor alignment with business needs. To bridge this divide, development teams are shifting away from data-first pipelines toward a product-first methodology structured as product, agent, data, and model. The Canvas framework provides a phased workflow moving from quick proof-of-concept validation to model orchestration and operational hardening. In Phase 4, developers focus on API management, external provider orchestration, cost optimization, and evaluation pipelines. Phase 5 adds necessary governance, compliance, user experience, and security layers required to transform working agent prototypes into sustainable production deployments.

  Mikiko Bazeley

• MongoDB · Sep 18, 2025

  MongoDB.local NYC 2025: Defining the Ideal Database for the AI Era