Michael Sylver - The Agent Operator

The Agent Operator

Sign in Subscribe

Michael Sylver

The Accountability Gap: Why Nobody Owns What AI Agents Produce

AI Operating Models

The Accountability Gap: Why Nobody Owns What AI Agents Produce

There is a question that most organizations deploying AI agents cannot answer clearly. Who is accountable for what this agent produces? Not in a general sense. Not "the team" or "the business unit" or "the AI governance committee." Specifically. When this agent produces output

The Most Valuable Professional of the Next Decade

The Most Valuable Professional of the Next Decade

Every generation has a professional archetype that commands disproportionate value. In the industrial era, it was the manager who could coordinate complex operations at scale. In the information era, it was the knowledge worker who could synthesize data and make fast decisions. In the software era, it was the developer

The Governance Imperative: Why Agent Operators Cannot Skip Risk Management

AI Operating Models

The Governance Imperative: Why Agent Operators Cannot Skip Risk Management

Governance is the Agent Operator responsibility most organizations skip. Direction is intuitive — you have to tell the agent what to do. Inspection is obvious — you have to check the output. Improvement makes sense — you want the agent to get better. Measurement is valued — you need to show ROI. But governance

How to Build Your First Agent Workflow

Agent Operators

How to Build Your First Agent Workflow

The best way to develop Agent Operator skills is to run an agent workflow. Not read about it. Not watch a demonstration. Run one — systematically, with the operating discipline that turns a one-time experiment into a repeatable business capability. Here is how to build your first one. Step One: Choose

The Skills That Make a Great Agent Operator

Agent Operators

The Skills That Make a Great Agent Operator

The five core responsibilities of an Agent Operator — direction, inspection, improvement, governance, measurement — describe what the role requires. But what separates a competent Agent Operator from a great one? The answer is a set of deeper skills that sit beneath the operational framework. These are not separate from the five

From Pilot to Production: Why Most AI Projects Stall

AI Operating Models

From Pilot to Production: Why Most AI Projects Stall

The pattern is familiar to anyone who has worked in an organization deploying AI. The pilot is impressive. The demo produces results that exceed expectations. The leadership team is excited. The investment decision is made. The project scales. And then it stalls. The agents that performed so well in the

The Inspection Mindset: How Agent Operators Maintain Quality at Scale

Agent Operators

The Inspection Mindset: How Agent Operators Maintain Quality at Scale

Of all the Agent Operator responsibilities, inspection is the most undervalued. Direction gets attention because it shapes what the agent does. Measurement gets attention because it demonstrates value. Governance gets attention because it manages risk. But inspection is where business judgment meets agent output — and where the difference between AI

Why AI Agents Fail Without Operators

AI Operating Models

Why AI Agents Fail Without Operators

AI agents are more capable than most people outside the technology industry realize. They can do significant work. They can research, analyze, draft, prioritize, and execute across complex workflows with a level of quality and speed that would have been impossible two years ago. And yet, in deployment after deployment,

The Agent Operator Is a Business Role, Not a Technical One

Agent Operators

The Agent Operator Is a Business Role, Not a Technical One

When people hear "AI Operator" they often imagine someone technical. A prompt engineer. A machine learning specialist. A developer who configures AI systems at a technical level. That is not what an Agent Operator is. The Agent Operator is a business role. The primary qualifications are business skills

The Difference Between Using AI and Operating AI

Agent Operators

The Difference Between Using AI and Operating AI

There is a capability distinction emerging in the modern workplace that most people have not yet named clearly. It is the distinction between using AI and operating AI. Most professionals are on the using side. A smaller number — and a rapidly growing one — are on the operating side. The gap

What Executives Get Wrong About AI Agents

AI Operating Models

What Executives Get Wrong About AI Agents

Most executives deploying AI agents today are making the same three mistakes. Not because they lack intelligence or commitment. Because the conventional wisdom about how to deploy AI is wrong in three important ways. Mistake One: Treating AI as a Technology Purchase The first and most common mistake is treating

AI Is Not Your Competitor. The Person Operating AI Is.

AI Is Not Your Competitor. The Person Operating AI Is.

The conversation about AI and careers is framed wrong. The dominant narrative is: AI is coming for your job. The agent will replace the human. Automation will eliminate roles. The future belongs to machines. This framing is wrong. Not because AI is not capable — it is increasingly capable. But because

The Agent Operator Loop: A Framework for Running Agents Toward Outcomes

AI Operating Models

The Agent Operator Loop: A Framework for Running Agents Toward Outcomes

Running AI agents effectively is not a one-time setup. It is a continuous operating discipline. The Agent Operator Loop is the framework that makes that discipline repeatable. It is a seven-stage cycle that takes an agent from objective definition to business outcome — and then starts again, with each cycle building

Output vs. Outcome: The Distinction That Changes Everything

AI Operating Models

Output vs. Outcome: The Distinction That Changes Everything

There is a distinction that most organizations deploying AI agents are missing. It is the distinction between output and outcome. Output is what the agent produces. Outcome is what the business achieves as a result. These are not the same thing. And confusing them is the primary reason that AI

The Human Layer: Why AI Agents Need Operators to Create Value

The Human Layer: Why AI Agents Need Operators to Create Value

AI agents are increasingly capable. They can research. They can write. They can analyze. They can prioritize. They can execute multi-step workflows across complex business processes without human intervention at each step. The technology works. And yet, in most organizations, the agents are not delivering the business value the investment

What Is an AI Operating Model — and Why Most Companies Don't Have One

AI Operating Models

What Is an AI Operating Model — and Why Most Companies Don't Have One

Most companies deploying AI agents today are making the same mistake. They are treating AI deployment as a technology decision. Buy the tools. Configure the agents. Train the users. Measure adoption. That approach produces AI activity. It does not produce AI outcomes. The missing ingredient is the AI operating model

No Code Required. Accountability Required.

Agent Operators

No Code Required. Accountability Required.

There is a persistent misconception about what it takes to operate AI agents effectively. The misconception is that it requires technical skills. Coding. Prompt engineering. Model fine-tuning. API integration. It does not. The most important qualification for an Agent Operator is not technical. It is accountability. And accountability is a

The Five Responsibilities of an Agent Operator

Agent Operators

The Five Responsibilities of an Agent Operator

Having an AI agent is not enough. Anyone can deploy an agent. Anyone can generate output. The question is whether that output becomes business value — and that requires a human operating layer that most organizations have not yet built. The Agent Operator is the professional who closes that gap. And

Every Job Is Becoming an Agent Operator Job

Every Job Is Becoming an Agent Operator Job

Everyone is asking the wrong question. The question is not: will AI take my job? The right question is: what does my job become when AI agents can do significant parts of it? The answer is not what most people expect. And understanding it early is the most important career

The Agentic Execution Gap: Why AI Agents Are Not Delivering

AI Operating Models

The Agentic Execution Gap: Why AI Agents Are Not Delivering

Every major enterprise has an AI initiative. Boards are asking about AI strategy. CEOs are setting AI mandates. Teams are experimenting. Billions of dollars are flowing into AI infrastructure, licensing, and implementation. And yet the outcomes are not matching the investment. Agents are being deployed. Output is being produced. But

What Is an Agent Operator?

Agent Operators Featured

What Is an Agent Operator?

Most companies do not have an AI agent problem. They have an Agent Operator problem. The agents are being deployed. The tools are being purchased. The workflows are being designed. But too often, nobody clearly owns what the agents produce. Nobody is accountable for whether the output is accurate, aligned,