AIAgentsandCopilots
A unified context plane where structured, unstructured, and vector data live together for reasoning and action.
Fragmented context produces hallucinating agents
AI agents need real-time context to reason and act accurately. In fragmented stacks the context is incomplete, stale, or scattered across vector stores, application databases, and event streams. Agents either hallucinate or fall back to static prompts that cannot reflect what is happening right now.
One plane for vectors, time-series, documents, and live state
MonkDB provides a single context layer where structured, unstructured, and vector data coexist on live system state. Agents can retrieve, reason over, and act on the same continuously updated truth, without round trips between systems. The platform turns prompt-based assistants into truly autonomous, context-aware operators.
Production-grade agentic foundations
Hybrid retrieval
Vector similarity, keyword search, and SQL filters in a single statement.
Live memory
Per-agent state and shared session memory backed by the same engine.
Tool execution
Agent actions land in the same plane that holds the context, with full audit trail.
Sovereign by default
Embeddings, prompts, and memory never leave your environment.
Give your agents the live context they need.
Talk to an engineer about your workload. We will scope a proof of value in your environment.
Request a demo