Enterprise Agent Tooling with MCP
One governed layer that exposes line-of-business systems to AI agents as tools, with identity, approval and audit on every call, so agents can act without being handed direct access.
Typical outcomes for this kind of engagement. Results depend on your data and starting point.
Agent pilots stop at the same place: the demo answers questions, and then someone asks it to do something. Letting an agent write to a core system means giving it credentials, and nobody wants to explain that to an auditor. So each team builds its own integration, with its own service account and its own idea of what an agent may do, and the second agent starts the work over from the beginning.
- Agents that can answer but cannot act, because nobody will grant them access
- Each team building its own integration, with its own service account
- No consistent record of what an agent did on whose behalf
- Write operations with no approval step between intent and effect
- The second and third agent repeating the first one's integration work
CloudGate builds one tool layer between the agents and the systems. Each capability is published as a Model Context Protocol tool through Azure API Management, called on behalf of the signed-in user rather than a shared service account, with write operations gated by an approval step and every call logged. Any agent — Copilot Studio, Azure AI Foundry, or one you have written — reaches the same tools under the same rules.
- Line-of-business capabilities published once as Model Context Protocol tools
- Calls made on behalf of the signed-in user, not a shared service account
- Write operations held for human approval before they take effect
- Rate limits, quotas and schema validation applied at the gateway
- Every tool call logged with the caller, the arguments and the result
- The same tools reachable from Copilot Studio, Foundry or a custom agent