Sixb Open-Sources Framework for Enterprise AI Operations
Key Takeaways
- ▸Sixb addresses a critical gap in enterprise AI: while MCP handles tool access, Sixb provides business context, operational rules, and governance required for safe AI agent operations
- ▸The framework uses ontology-driven architecture where business entities and rules are defined declaratively, enabling AI agents to operate safely within enterprise constraints while respecting permissions and workflows
- ▸The open-source release includes Northline Operations, a fully functional example demonstrating real-world scenarios like equipment-triggered service dispatch with approvals, coverage rules, technician recommendations, and cross-system workflows
Summary
Sixb has open-sourced a framework designed to model the operational layer of enterprise AI systems. While most companies use AI assistants like ChatGPT, Claude, or Gemini today, these remain isolated assistants lacking business context, operational rules, and governance. Sixb addresses this gap by providing a framework to connect data sources (CRMs, ERPs, file systems), model business entities and relationships, define operational rules and permissions, and execute workflows.
The framework solves what MCP (Model Context Protocol) doesn't: it goes beyond tool access to model business context, permissions, and governance. Users define an ontology describing their business domain, similar to TypeScript type definitions, and can specify actions that AI agents can safely perform with human oversight. The project includes Northline Operations, a complete example showing how an AI system can handle equipment alerts in a field service company—creating service cases, applying coverage rules, recommending technicians, requesting approvals, and executing work orders.
