I pioneered machine teaching at Microsoft. Building AI agents is like building a basketball team, not drafting a player
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.
The Testing Illusion
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.
How Machine Teaching Creates Practice
Machine Teaching provides the structure that modern agentic systems need. It guides agents to:
- Perceive the environment correctly.
- Master basic skills that mirror human operators.
- Learn higher-level strategies that reflect expert judgment.
- Coordinate under a supervisor agent that selects the right strategy at the right time.
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.
Why CEOs and Leaders Need Practiced AI
Practice is what drives true autonomy in agents. I invite every leader to begin reframing a few assumptions:
- Stop thinking in terms of models and think in terms of teams. Every day interactions with systems like ChatGPT or Claude are powerful, but they reinforce a misconception that large language models are the path to enterprise autonomy. Autonomy emerges from specialized agents that take on perception, control, planning and supervisory roles through a wide variety of technologies.
- Identify where expertise is disappearing and preserve it within agents. Many essential operations rely on experts who are nearing retirement. CEOs should ask which processes would be most vulnerable if these experts left tomorrow. Those areas are the ideal starting point for a Machine Teaching approach. Let your top operators teach a team of agents in a safe practice environment so that their expertise becomes scalable and permanent.
- Recognize that you already have the infrastructure for autonomy. Years of investment in sensors, MES and SCADA systems, ERP integrations and IoT telemetry already form your organization’s backbone of digital twins and high-fidelity simulations. Success requires orchestration, structure, and leveraging the data foundation you already built.
The Payoff of Practice
When enterprises give agents room to practice before deployment, several things happen:
- Human teams begin to trust the AI and understand its boundaries.
- Leaders can calculate true ROI rather than speculative projections.
- Agents become safer, more consistent and aligned with expert judgment.
- Human teams are elevated rather than replaced because AI now understands their workflows and supports them.
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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