NVIDIA shows what works when companies scale AI agents

Companies that make AI agents actually work measure things from day one. AT&T's "Ask AT&T" platform handles over 100 different use cases now because they built in ways to track and improve from the beginning. The system learns from real employee and contractor questions instead of guessing what might help.

The examples that work well have something in common. ChipNeMo serves 5,000 NVIDIA engineers because they trained it on internal chip design data first—they solved the "does it understand our work?" problem before worrying about speed. Pegatron's manufacturing agents plug into their existing systems instead of running separately. These teams treated agents as infrastructure choices, not tools bolted on later.

If you're on a legal or product team, NVIDIA's measurement framework gives you a compliance structure. When agents interact with customers, process internal data, or make operational decisions, you need to track adoption, accuracy, and business results. That shows responsible use, not just financial returns.

The governance frameworks you build now will shape how teams work in a few years.

AI On: 6 Ways AI Agents Are Raising Team Performance — and How to Measure It

AI agents are expected to be involved in most business tasks within three years, with effective human-agent collaboration projected to increase human engagement in high-value tasks by 65%.

NVIDIA BlogErik Pounds