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.
Just as human teams develop expertise through repetition, feedback and clear roles, AI agents must develop skills inside realistic practice environments with structured orchestration. Practice is what turns intelligence into reliable, autonomous performance.
Many enterprise leaders still assume that a few major LLM companies will develop powerful enough models and massive data sets to manage complex enterprise operations end-to-end via “Artificial General Intelligence.”
But that isn’t how enterprises work.
No critical process, whether it be supply chain planning or energy optimization, is run by one person with one skill set. Think of a basketball team. Each player needs to work on their skills, whether it be dribbling or jump shot, but each player also has a role on the team. A center’s purpose is different from a point guard’s. Teams succeed with defined roles, expertise and responsibilities. AI needs that same structure.
Even if you did create the perfect model or reach AGI, I’d predict the agents would still fail in production because they never encountered variability, drift, anomalies, or the subtle signals that humans navigate every day. They haven’t differentiated their skill sets or learned when to act or pause. They also haven’t been exposed to expert feedback loops that shape real judgment.
Machine Teaching provides the structure that modern agentic systems need. It guides agents to:
Take one Fortune 500 company I worked with that was improving a nitrogen manufacturing process. Our agents practiced inside the AMESA Agent Cloud, improving through experimentation and feedback. In less than one day, the agent teams outperformed a custom-built industrial control system that other automation tools and single-agent AI applications could not match.
This resulted in an estimated $1.2 million in annual efficiency gains, and more importantly, gave leadership the confidence to deploy autonomy at scale because the system behaved like their best operators.
Practice is what drives true autonomy in agents. I invite every leader to begin reframing a few assumptions:
When enterprises give agents room to practice before deployment, several things happen:
Agents won’t truly perform without experience, and experience only comes from practice. The companies that invest in and embrace this framing will be the ones to break out of pilot purgatory and see real impact.
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