There’s a common misconception that AI and LLM-driven agents can just “figure out” any task. That, given enough data and compute power, they’ll magically generate perfect results without much human intervention.

That’s not how it works by my observations.

Some tasks agents do fine without human intervention, but that is not true for all tasks. AI is not an oracle, yet. Today and for some time it won’t be a fully autonomous problem-solver for every problem. There is no doubt that it’s a force multiplier—one that’s only as good as the person driving it towards a desired outcome. The better you define the outcome and align the agent to it, the better the result will be. If you fail to steer it properly, or worse, don’t even recognize when it’s veering off course, the outcome will be equally off target.

The Illusion of AI Autonomy

Let’s break this down.

You’ve got an LLM-powered assistant writing requirements for a new feature. It spits out something that looks good at first glance. It’s structured well, grammatically correct, even using the right jargon. But when you dig deeper, you realize it misunderstood core business constraints, overcomplicated a simple feature, or left out a critical user need.

Whose fault is that?

The AI? No.

The person prompting it? Partially.

The real issue? A lack of precise alignment between intent and output.

AI doesn’t have an innate sense of correctness. It only mirrors patterns from the data it was trained on, shaped by the inputs and feedback it receives. If the feedback loop is weak or the desired outcome isn’t well defined, the model will confidently produce incorrect or misaligned results.

The Human Role in AI Success

This means that the quality of AI-driven work is only as strong as the human guiding it.

• If you define the wrong outcome, the AI will chase the wrong goal.
• If you fail to recognize when the AI is drifting, it will continue on a bad trajectory.
• If you provide poor feedback, it will reinforce bad patterns and biases.

This is why skilled AI agent operators will outperform those who blindly rely on automation. Knowing what good looks like and being able to course-correct when things go wrong are the real differentiators between success and failure in today’s AI-driven workflows.

What I Observed

I’ve spent a lot of time exploring coding agents and their ability to autonomously build everything from simple scripts to complex applications. And while they can be impressive, there’s a recurring pattern: they eventually hit a wall—a bug or logical flaw they just can’t overcome.

What’s fascinating is how they handle it. Often, they charge ahead with brute force, guessing fixes, cycling through the same incorrect fixes, completely unaware that they’re stuck. They exude confidence in fixes that ultimately fail. If I weren’t experienced enough to spot these errors, I’d have no way of guiding them out of the mess they create.

But you know what, even with these bugs from time-to-time, it’s still a much better coder than me in many ways. It’s faster, more precise, and often more elegant in its solutions. The frustration for me only kicks in when it veers off course, and that’s where my human intervention becomes critical. Reviewing, testing, and course-correcting the agent’s output is the key to making it truly useful.

Today’s AI agents don’t need to be perfect. They just need the right human in the loop, with knowledge of their task, to keep them on track.

The AI Compass Principle

To consistently achieve high-quality AI-assisted work, apply The AI Compass Principle:

👉 “The effectiveness of an AI agent is directly proportional to the clarity of the goal, the precision of its alignment, and the human’s ability to detect and correct deviations.”

Most LLMs agents today are not autonomous experts; they are extensions of your thinking. If you want them to deliver better results, sharpen your ability to define, align, evaluate, and correct. The best AI outputs don’t come from the best models, they come from the best operators.

“Shit in equals shit out.”

If you want to explore this with me, let’s connect.