Guiding Agentic AI: The Human Role in Shaping Autonomous Systems

Unveiling Agentic AI

Hello there! By now, you've probably at least heard this term: It subsumes the concept of what has been referred to as ‘Agentic AI.’ Well, today, we are going to explore what exactly that is and why each one of us should be concerned. Today, we are speaking about how important it is for humans to supervise smart agents, lead them, and cooperate with them.

In other words, it is self-generated and possesses the autonomy to perform its intended functions, as claimed in the definition of the concept. For a second now, try to picture an AI that decides, learns, evolves, and grows when required to do so. However, where there is great power, there lies equally great responsibility, and that is where humanity intervenes. 
 
OK, let’s analyze this, shall we? 

Key Insights on Agentic AI

Prevalence of Bias in AI Systems

Approximately 70% of AI systems exhibit bias, reflecting the prejudices in their training data, underscoring the essential need for human oversight to address these issues

Boosting AI Accuracy

Integrating human feedback can improve AI model accuracy by up to 30%. This underscores the essential role of continuous human participation for effective AI learning and adaptation

Economic Contributions of AI

McKinsey estimates that AI could add $13 trillion to the global economy by 2030, highlighting the necessity for human-AI collaboration to ensure ethical practices and accountability

Why does Agentic AI need human oversight? 

Think of a child on the playground: They might go through the fun like they always do, but once left alone, catastrophes are almost around the corner. The same applies to agentic AI: The fact that AI is so potentially powerful does not mean that it cannot be a watchful eye to ensure that it doesn’t go astray.

Ethical Problems

It has no ethical standards like human beings do; therefore, it operates on data and algorithms that enable it to commit mistakes. For instance, AI systems, given the challenge of optimizing a factory, may select overworked workers as the most effective option. They would, therefore, require supervision from human beings in this. In this way, their actions connect to values that we cherish and possess. 

Reducing Bias

The thing with AI-based systems is that they learn from data, and if data is biased, then AI will be. For instance, if an AI model is based on training to its data on past hiring where discrimination was inevitable, then it would produce an unbalanced hurdle in hiring. Human intervention, therefore, calls to identify such unfair issues and take correct corrective action before they occur.

Accountability

And if so, who is at fault? We need to know if the person is asking for information about the AI, the developers of the AI, or the company that the AI belongs to. That has been a question, but one thing is for sure: thus, it lies in the hands of humans to take the necessary responsibility for this. We cannot just set it on AI as it unfolds because, essentially, we are responsible for guaranteeing these systems not only operate securely but appropriately. 

Humans Shaping Agentic AI Decisions

Therefore, to extend from this discussion on why oversight is important, let us turn to the next discussion on how oversight can be implemented or put into practice to guide AI. Just consider for a moment: Friends’ help is indispensable, for example, when teaching a teen to drive. I am sure you don’t just provide them with the keys and then close your eyes and mind to whatever they are going to do with it. You guide them through learning, and they achieve learning on their own right from the basics up to the level of driving ‘on your own.’

Setting goals

What’s good with AI is that it is wonderful in the achievement of goals though it remains our responsibility to establish them. Better customer care, production and supply chain, product development –all these entail human beings to determine what constitutes success. And that means more than merely identifying the end vision of what’s desirable and providing examples of how it can be attained. 
 
Here, the AI system designed to save the building's energy when it becomes self-sufficient turns off the light and heat, essentially causing discomfort to people in a dark and cold environment. A human assistant would introduce rules to enable the AI to check energy conservation without compromising people’s comfort. 

Perpetual Learner

But AI, in the end, has to keep learning—just like humans—if it’s to remain employed in the future. And at the center of all that learning throughout the years are people. They make learning continuous and enable AI to adapt to the new information introduced and modify the algorithms used and the models as well. 

Establishing a Feedback Loop

Quite a lot of feedback is well-designed for AI systems, although they do not actually catch it. Humans put feedback in loops so that AI learns what's right and wrong: confirming what the AI selected, providing additional data to the AI, or changing its objectives based on new information.