This project provides a Model Context Protocol server that enables autonomous agents to interact with and manage automation workflows. It functions as an integration layer, allowing language models to discover, build, test, and deploy complex automation sequences through natural language instructions and structured schema-based communication.
The platform distinguishes itself by offering granular control over automation logic, including the ability to perform surgical, incremental patches to specific workflow nodes rather than replacing entire structures. It supports multi-instance connectivity, allowing agents to dynamically switch between development, testing, and production environments while maintaining session-scoped isolation to prevent data interference.
Beyond core orchestration, the system includes comprehensive administrative utilities for security auditing, credential management, and resource tracking. It provides diagnostic tools for monitoring execution history and verifying system health, alongside search and discovery features for navigating integration nodes and pre-built workflow templates.
The platform is designed to be installed as a bridge between artificial intelligence agents and automation services, facilitating the full lifecycle management of business processes.
