Salesforce’s latest agent testing/builder tool and Jeff Bezos’s new AI venture focused on practical industrial applications of AI show that enterprises are inching towards autonomous systems. It’s meaningful progress because robust guardrails, testing and evaluation are the foundation of agentic AI. But the next step that’s largely missing right now is practice, giving teams of agents repeated, structured experience. As the pioneer of Machine Teaching, a methodology for training autonomous systems that has been deployed across several Fortune 500 companies, I’ve experienced the impact of agent practice while building and deploying over 200 autonomous multi-agent systems at Microsoft and now at AMESA for enterprises around the globe.
Every CEO investing in AI faces the same problem: spending billions on pilots that may or may not deliver real autonomy. Agents seem to excel in demos but stall when real-world complexity hits. As a result, business leaders do not trust AI to act independently on billion-dollar machinery or workflows. Leaders are searching for the next phase of AI’s capability: true enterprise expertise. We shouldn’t ask how much knowledge an agent can retain, but rather if it has had the opportunity to develop expertise by practicing as humans do.
