What makes this workflow a good candidate for AI evaluation?
A checklist of the properties that make facility booking suitable for evaluation: defined start and end, several systems, unmet conditions, exceptions, role limits and a handover.
Use this list to judge whether the workflow suits your catalogue and your customers' evaluation needs.
- **Clear start:** an approved facility with stated limit, rate, term and conditions that a grader can read. - **Clear end:** a booked facility whose records can be compared field by field with the approval. - **Several systems touched:** the loan system, security register, document store and payment setup all change. - **Conditions that may be unmet:** the correct action is sometimes to hold or escalate rather than proceed. - **Exceptions:** rate overrides, split drawdowns and approvals amended after booking has begun. - **Checkable outcomes:** results can be read from final state and action log, which keeps grading objective. - **Handover to servicing:** the booking must give the next team what it needs to start. - **Role boundaries:** rules about who may override a rate let policy-following be tested as well as accuracy.
Use these properties to decide quickly whether the workflow fits your catalogue and gives your customers objective, repeatable evaluation cases.
- Audience:
- Licensed resellers
- Content type:
- Checklist
- Buyer question:
- What it is and why it matters
Content release: r-a220eb6e099b243b
Related questions
- What is loan booking and why does it matter for testing AI agents?
- How does the booking workflow map onto record links and agent tool actions?
- What does a booking error look like when an agent misses an approval condition?
- What makes recording an approved facility hard for an AI agent?
- Why is this workflow a better test for agents than question answering?
- What does booking an approved facility involve in a real lender?