AI governance
Agentic AI went commercial. Traceability is what to buy on
Once an agent is billed for completing tasks rather than demonstrated against benchmarks, the question stops being how well it scores and becomes whether it finished, and whether it can prove it.
Agentic AI has moved from demonstration to billed production, and that changes the question a buyer should be asking. A benchmark score describes how a model performs on a public test set. It says nothing about whether an agent completed the task you gave it, inside your estate, against your data, and left a record you could hand to an auditor.
For a regulated or public-sector operator in Singapore, the second question is the only one that survives procurement.
What to specify instead of a benchmark
- Full action logging: every call the agent made, in order, with inputs and outcomes, retained long enough to matter.
- Defined failure boundaries: the conditions under which it stops and escalates rather than continuing on a bad assumption.
- A named person at the escalation point, not a queue.
- Cost per completed task, so the commercial model rewards finishing rather than attempting.
Why this lands on the integrator
None of those four are model features. They are properties of the system built around the model, which is integration work, and which is why an agent bought as a product tends to arrive without them.
Traceability in particular is an architecture decision made early or not at all. Logging that was not designed into the interface boundary cannot be recovered from it afterwards.