AI AGENTS
Prototypes are easy. Production agents aren't.
Sophisticated models alone don't make agents reliable. Context does. MongoDB gives you everything you need to do it on one platform.
Why production agents are built on MongoDB
Real-time, accurate retrieval
Run vector, full-text, and hybrid search alongside graph traversals on live operational data, so agents get the most relevant, current context in one query.
Agents that remember
Persist conversation history, tool results, and learned context as documents alongside your operational data so agents recall past interactions, adapt over time, and recover cleanly from restarts.
Built for scale
Start with a single workload and scale to global agent fleets on the same platform, with workload isolation, elasticity, and mission-critical resilience. Growth will never force a ground-up rewrite or migration.
Secure by default
Agents inherit encryption, IAM, tenant isolation, and auditing from a platform already proven in high-trust, regulated environments, so security, compliance, and policy enforcement are built in.
Run your choice of model anywhere
Use a consistent document model, query API, and security layer across AWS, Microsoft Azure, Google Cloud, on-premises, and air-gapped or sovereign environments, reducing lock-in as your agent strategy evolves.
Audit every action taken on your data
Trace every query, tool call, and decision back to underlying data, with visibility into whose authority an agent is acting on so you can prove compliance, contain risk, and investigate issues fast.
Every retrieval capability an agent needs
Agents in production
FAQ
Better models and longer context windows don't solve the problem agents actually run into in production: hallucinations. Hallucinations aren't a model failure; they're a retrieval failure. A smarter model still hallucinates if it can't reach the proper subset of current, trustworthy data at the moment it needs to act.
This is usually a persistence problem masquerading as a reliability problem. State (what a single run produces) and memory (what an agent carries between runs) have different lifecycles; treating them as one leads to reports of "unreliability." Recovering cleanly from an interruption is a baseline requirement for production agents, so memory has to persist scalably and performantly.
Memory, governance, eval, and orchestration aren't features bolted onto a workflow engine; they’re separate product categories. Building it all in-house is a major engineering commitment. The practical approach is to build what's specific to the business and buy the technology categories underneath it.
This is a coordination problem that shows up as a security gap. Without a shared tool registry, every new agent you ship results in duplicated engineering effort, extra security exposure, and increased technical debt. The solution is centralized visibility into what tools exist, who owns them, and what's been reviewed, not another one-off fix per team.
Not with the right database. Agentic coding is iterative; small changes shouldn't require stopping to write a migration script. MongoDB's document model lets the schema adapt to the code, not the other way around. The same applies to the whole stack: one platform for transactions, search, analytics, and vectors avoids the sprawl that slows agents and humans down.
Start building agents with MongoDB Atlas
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