The first wave of enterprise GenAI answered questions. The second wave completes work. Agentic systems — models that plan, call tools, check results and iterate — are moving from research demos into settlement desks, operations rooms and reservation back offices.
The design question is no longer "can the model do it?" but "how do we supervise it?" In our deployments, three patterns separate production agents from expensive incidents: checkpointed autonomy (agents propose, humans approve, thresholds widen with track record), full action logging (every tool call auditable after the fact), and evaluation harnesses that regression-test agent behavior like any other software release.
In travel, we watch agents assemble day-wise itineraries with live pricing in under two minutes — work that took a skilled consultant three hours. In finance operations, reconciliation agents chase, match and escalate with a diligence no human sustains at 4 p.m. on a Friday. The workforce implication is not replacement but re-leveling: people move from doing the process to owning its exceptions and its improvement.
Our rule of thumb for 2026: automate the middle of the workflow first. Intake and judgment stay human longer; the transport, transformation and follow-up between them is where agentic AI pays for itself fastest.
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