Agentic AI shifts the question from 'what can the model do' to 'what is the agent responsible for'. That shift requires product thinking, not just prompt thinking.
Every agent needs an owner, a user, a success metric, a feedback loop, and a lifecycle. Without those, you have an experiment, not a product — and experiments do not earn an operating budget.
We help organisations stand up a thin product layer around their first wave of agents: a backlog, a release cadence, an observability stack, and a sunset policy. The discipline is unglamorous and absolutely essential.
The leaders who treat agents as products will compound advantage. The ones who treat them as features will spend the next two years debugging hallucinations.