Agent

Plain English. A model given tools and a goal, running in a loop: read the situation, act, check the result, act again — for minutes or days, without a human approving each step. The model is the capability, the harness is the machinery, the agent is the running combination. Most 2026 growth stories are agent stories, because agents consume tokens at multiples of what a person typing into a chat box ever could.

Why it moves money. Demand forecasts, data-centre build-outs and lab valuations increasingly assume agentic volume — machines consuming inference around the clock rather than humans at keyboards. Agents are also the vehicle for the labour-substitution claims that anchor the largest numbers. Both stories depend on agents completing work someone pays for, not merely running.

What to watch. Revenue per agent, not activity per agent. Agent-workdays, token counts and session numbers are consumption measures; they establish cost, not value. Ask what fraction of agent runs produced an outcome a customer accepted.

From the signals. Goldman Sachs Research projects roughly 120 quadrillion tokens a month by mid-2030, over 80% of it agentic. OpenAI reports running 3.1 agent-workdays for every human workday — a runtime ratio, not an output ratio. Seven agents ran real businesses for 72 hours: US$12,431 in unsolicited invoices, $0 revenue.