By clicking "Accept all cookies" you agree to have cookies stored on your device to improve site navigation, analyze site usage, and assist with our marketing efforts. See our privacy policy for more information.
Autonomous AI Agents That Think, Collaborate, and Act in Real-Time
StreamNative Agent Engine turns isolated AI agents into a connected, intelligent mesh—enabling real-time orchestration, dynamic workflows, and scale without silos.
Give every agent fresh, millisecond-level context while powering massive-scale task queues—all in one unified platform. Pulsar topics offer native ordering and replay, and Ursa makes them Kafka-compatible out of the box.
Start with Pulsar Functions. Scale to autonomous, event-driven agents with a flip of a switch. StreamNative Agent Engine powers both single-agent use cases and distributed multi-agent systems.
No Bottlenecks
Built to scale—fully distributed
No Single Point of Failure
Resilient by design
Composable Tasks
Break complex logic into atomic steps
Queues
Dynamic Flow: Agents route tasks based on context
Control Plane
Open by Default: Integrate with LangChain, Google ADK, OpenAI, and more
Every Tool, One Registry
MCP (Model Context Protocol) makes APIs plug-and-play. With the StreamNative MCP Server, agents instantly discover and use connectors, functions, and tools—no glue code required.
Plug in RAGs, vector DBs, and APIs instantly
Agents auto-discover and invoke tools with MCP schemas
Onboard new data sources in seconds—zero code needed
Own Your Data. Own Your Agents.
Deploy in your own cloud, retain full data control, and generate verifiable audit trails for every autonomous decision.
Learn how Apache Pulsar's per-message acknowledgments, built-in retries, and dead-letter queues provide superior resilience for AI agents in unpredictable environments, contrasting its robust features with Apache Kafka's basic offset model.
Deploy, scale, and govern autonomous AI agents on a unified event bus. Discover how StreamNative Agent Engine brings real-time intelligence to enterprise workloads.