Most AI orchestration frameworks are built with the assumption that model calls and function execution happen within the same process. This approach, while straightforward, introduces several limitations when building production-grade AI applications quickly.Inferable takes a fundamentally different approach with its distributed control plane architecture.
The control plane is the central component of Inferable, responsible for orchestrating the execution of workflows and managing their state.A cloud version of it is hosted and maintained by Inferable at api.inferable.ai.It’s a stateful API server with a persistent storage layer for workflow state.
Inferable’s control plane architecture enables workflows to run in isolated environments, including private VPCs, while maintaining communication with the central control plane through long polling.
If you’re doing long running LLM-based workflows, like a large batch processing job that takes hours or an agent execution that takes minutes, you can pause the workflow at any point and resume from the same point later.The control plane will:
Maintain the workflow state for the entire duration
Handle any intermittent issues with the execution
Resume from the appropriate point if there are interruptions
Provide visibility into the workflow status throughout its lifecycle in the Inferable app