"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"
"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."
Enterprise progress toward autonomous systems requires more than testing tools and guarded evaluations; it requires structured practice for agent teams. Machine Teaching provides a methodology for training autonomous systems through repeated, orchestrated experience, yielding expertise rather than mere knowledge retention. CEOs face expensive pilots that often fail to translate demo performance into real-world autonomy because agents lack practical, role-based repetition and feedback. Critical enterprise processes require coordinated multi-skill teams, not single models. Developing reliable autonomy depends on environments that enable agents to practice, receive feedback, and refine coordinated behaviors under realistic conditions.
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