From Generative AI to Agentic Credit Workflows

There is an important distinction between generative AI and the increasingly discussed idea of agentic AI.

Generative AI produces something when asked.

An agentic system can understand an objective, determine intermediate steps, interact with information, and help move a process toward completion.

Credit underwriting is naturally suited to this model.

Consider a new credit request.

A useful system should be capable of helping determine:

·  What is being requested?

·  Which entities and facilities are involved?

·  What documents are available?

·  What information remains missing?

·  What financial and qualitative analysis must be completed?

·  What diligence questions arise from that analysis?

·  What approvals are required?

·  Is the file sufficiently complete to move forward?

This begins to resemble a junior underwriting team embedded into the workflow.

But there is an important boundary.

Credit Copilot is not being designed to make the final lending decision.

A machine can help organize evidence and identify patterns. Accountability for extending credit should remain with authorized human decision-makers.

The architecture I am exploring is agentic assistance with human decision authority. 

That distinction will matter increasingly as AI moves from experimentation into institutional workflows.