nexus

Multi-channel deployment allows you to expose a single Python workflow simultaneously as a REST API and a CLI tool. This eliminates the need to build and maintain separate interfaces for different access methods.

MCP deployment for AI agents is the process of exposing workflows as an MCP service, enabling AI assistants to execute tasks and query data via natural language. It integrates with workflow orchestration to connect agent requests directly to backend logic.

You maintain session state across API, CLI, and MCP channels using unified sessions, which ensure consistent state management across all deployed interfaces. This allows a single workflow to track context regardless of the access channel used.

Multi-channel workflow deployment includes built-in authentication and rate limiting features such as JWT, RBAC, and input validation. These enterprise security features are provided out of the box to protect deployed API, CLI, and MCP endpoints.

You can use Kailash SDK for workflow orchestration with API and CLI deployment through multi-channel service integration. The SDK handles backend orchestration while the deployment layer manages interface generation and unified session management.

A limitation of deploying workflows as multi-channel services is that it relies on Python and Kailash SDK for orchestration, meaning non-Python workflows require additional integration layers. The unified session design also requires all channels to share a compatible state model.