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Posts connected to the mcp topic.
A reference view for cloud architects: run AI Rule Engine in your own Azure subscription, point AI steps at your own Azure OpenAI resource, and expose governed decisions to AI agents over MCP - with the data boundary drawn where your compliance team expects it.
Thousands of MCP servers hand your agent data. Almost none hand it decisions. AI Rule Engine turns your business logic into governed, deterministic MCP tools - with per-agent permissions, explainable runs, and human approval gates an agent can carry but never bypass.
Workflows that pause for human review no longer dead-end an AI agent. Over MCP, your assistant can read the pending form, submit your answers on your behalf, and bring back the final result — with a per-action switch that keeps in-person approvals in person.
AI Rule Engine now supports MCP so AI assistants can initiate workflows, read files, inspect ruleset run logs, and operate with per-instance permissions.
Why human oversight matters in AI processes, and how AI Rule Engine connects AI providers, governed workflows, and MCP-enabled assistants in one controlled system.