‍An MCP connection gives an AI assistant access to tools. The infrastructure behind that connection determines whether it can take part in real loan operations, with the right controls in place.

Connecting an AI assistant to a loan platform can make loan data easier to query. Moving from questions to actions is a bigger step. Reading a loan term returns information. Changing that term affects a live record that other operational work may depend on.

For many private credit teams, that risk has kept AI agents on the read side of the loan book. The agent can find or analyze information, but people still have to carry the work into the loan system and make the change themselves.

MCP can provide the connection that lets an AI assistant use tools exposed by another system. But a connection alone does not establish what the agent may do, how a proposed change gets reviewed, or who has authority to approve it.

A read tool returns data. A write tool can initiate a change. In a loan operation, that could mean preparing a term update, recording a transaction, or adding a fee.

The difference is more than technical. The agent must identify the relevant loan and prepare the requested change in a form the team can examine. The platform must preserve the existing approval process and prevent a proposed change from taking effect before the required approval.

OWASP identifies excessive agency as a risk when AI systems have more functionality, permissions, or autonomy than a task requires. Its guidance recommends limiting tool capabilities and permissions, and requiring approval for high impact actions.

For private credit, that means write access should be designed around specific operational tasks. A tool that prepares a defined loan change is easier to govern than broad access to edit records. And the proposal should enter the loan platform’s workflow, where the team can review it, rather than being applied directly from the AI conversation.