Executive summary
4
requirements to specify in place of a benchmark score
Key conclusions
- A benchmark score does not show whether an agent finished a task in your systems, using your data, and left a record you can give to an auditor.
- Full action logging, defined failure boundaries, a named escalation contact and cost per completed task must all be built around the model. An agent bought as a product usually arrives without them.
- Traceability is an architecture decision. If it is not made early in the design, it cannot be added later.
Agentic AI has moved from demonstration to billed production, and that changes the question a buyer should ask. A benchmark score describes how a model performs on a public test set. It does not show whether an agent completed the task you gave it, in your systems and against your data, or whether it left a record you could give to an auditor.
For a regulated or public-sector operator in Singapore, procurement decisions turn on that second question.
Specify four requirements in place of a benchmark score
- Full action logging: every call the agent made, in order, with inputs and outcomes, kept for as long as an audit or investigation may need it.
- Defined failure boundaries: the conditions under which the agent stops and escalates.
- A named person, rather than a shared queue, who receives each escalation.
- Cost per completed task, so that the commercial model pays for finished work.
These requirements are integration work, so the integrator must deliver them
All four are properties of the system built around the model. Building them is integration work, which is why an agent bought as a product usually arrives without them.
Traceability in particular has to be decided early in the architecture. If logging was not designed into the interface boundary, it cannot be recovered from that boundary afterwards.
