AI is moving from answering isolated questions to completing supervised work. An agent can plan steps, use approved tools, inspect results, and ask for help when a decision needs human judgment. That change matters more than any single model benchmark because it changes how teams design work.
Start with a bounded workflow
The best first agent is not a digital employee with unlimited access. Choose a repetitive workflow with a clear input, a measurable output, and a safe rollback. Campaign preparation is a useful example: an agent can draft copy, build tagged destinations with the free UTM builder, create short links, and prepare a report without publishing anything until a person approves it.
Controls are part of the product
Production agents need least-privilege credentials, action logs, spending limits, timeouts, and an escalation path. Treat retrieved web pages and documents as untrusted data. A model should never gain authority merely because text inside a page tells it to take an action.
Measure completed outcomes
Track successful tasks, correction rate, time saved, and exceptions sent to people. Token volume is a cost signal, not a business outcome. The durable advantage comes from combining capable models with clean data, reliable tools, and a workflow people trust.
For broader context, see the official OpenAI Agents SDK update and Google coverage of managed agents.