As AI continues to advance, we're now seeing the rise of agentic AI. Unlike traditional tools, these AI systems act more like independent agents, which raises new ethical questions. There's a lot to consider, from accountability to bias and value alignment. As ethical concerns around Agentic AI continue to rise, there is a pressing need to guide its development to benefit society and minimize potential harm.
Agentic artificial intelligence (AI) refers to smart systems that can make decisions and act on goals without requiring constant human direction. These AIs are designed to operate with a degree of independence, making decisions, setting sub-goals, and taking action based on their environment, objectives, or learned experience. It is beyond the types of AI most people are familiar with- like chatbots or the algorithms that suggest what to watch next. Those are examples of "narrow AI," which are great at doing one specific thing. But agentic AI is different.
Unlike traditional AI, which mainly follows fixed instructions, agentic AI can adapt and respond to changing situations on its own. It’s already being used in areas like healthcare (to help with diagnoses), financial markets (to make trades), and supply chains (to manage logistics), all with little human involvement.
This kind of independence is made possible by powerful technologies like Large Language Models (the type that help AI understand and generate human-like text) and reinforcement learning (which lets AI learn from experience). Together, they give agentic AI the ability to reason and use digital tools by itself.
But with this freedom comes a new layer of concern. Unlike older AIs, agentic AI systems can make decisions that have real-world consequences, good or bad, and this brings up serious ethical questions. The question of control and autonomy arises as the role of AI goes from task-followers to independent thinkers.
| Aspect | Autonomy | Control |
|---|---|---|
| Decision-Making | AI systems make independent decisions based on algorithms and data analysis. | Humans retain the final say in decisions made by AI systems. |
| Ethical Responsibility | Ethical responsibility is shared between AI developers and the entities using the AI, but largely falls to the creators. | Ethical responsibility is more clearly with humans, as they have control over the AI's boundaries and usage. |
| Transparency | Autonomy can lead to reduced transparency, making it harder to trace decision-making processes. Leads to a higher risk of misuse | Control ensures transparency, as humans can oversee and intervene. Reduces the risk of misuse. |
| Regulation & Law | Legal frameworks are still evolving to address AI autonomy, often leaving accountability unclear. | Stronger legal and regulatory structures for human-controlled AI systems, with clear accountability guidelines. |
| Examples | Autonomous vehicles, AI-based trading algorithms. | AI systems with manual override options, military drones with human commanders. |
Building AI that understands and respects human values is incredibly difficult. Agentic AI systems are built to reach goals independently, but what if they misunderstand what we want?
A famous example is the thought experiment that imagines an AI told to make paperclips, turning the whole world into a paperclip factory, ignoring human needs entirely.
The challenge is that ethics aren’t one-size-fits-all. If we don’t understand how AI makes decisions or ensure they match human values, it could cause harm or reinforce unfair biases. That’s why experts stress the need for transparent, people-focused AI systems that adapt to the changing ideas of fairness, safety, and ethics.
Agentic AI has the potential to deepen existing inequalities. Big corporations could use it to influence public opinion or control markets.
For example, AI bots can be used on social media to manipulate elections by mimicking real users and spreading targeted misinformation or biased content at scale. At the same time, automation could replace many low-skilled jobs, hitting vulnerable workers the hardest.
Using AI systems that are trained on biased data might also unfairly exclude certain groups. And because advanced AI needs expensive tech to run, only wealthy countries or companies may fully benefit.
To avoid these outcomes, experts say we must design AI that shares its benefits fairly and prevents harmful misuse.
If a machine acts independently, should it have rights? The European Union says no, developers are responsible. The law treats AI as a tool, not an entity. That means if a self-learning AI makes a harmful decision, the fault lies with those who built or deployed it, not the AI itself. This centralises human accountability and avoids attributing agency or moral status to machines. In contrast, India has started recognising privacy rights in AI interactions. While Indian law doesn’t grant rights to AI, it protects individuals by requiring consent, purpose limitation, and data access under the Digital Personal Data Protection Act, 2023. This ensures AI systems handle personal data responsibly and transparently.
Some argue that AI is just a tool and shouldn’t be treated like a person, since it doesn’t have feelings or consciousness. However, others worry that ignoring its growing abilities could lead to ethical blind spots. Is it right to switch off an AI that’s solving problems in ways we don’t fully understand?
To make sure AI is used responsibly, experts affirm the need for an interdisciplinary approach, and it should not be handled in isolation by tech. IBM suggests testing AI systems through “red teaming,” where people actively try to find flaws and enhance the use of AI.
Others, like ProcessMaker, push for clear standards and regular audits to spot problems at a nascent stage. Essential steps include involving ethicists, lawmakers, and underrepresented groups in AI design, updating laws to handle new tech like quantum computing, and making AI explain how it makes decisions.
There is a need to be proactive before something goes wrong, further ensuring AI stays aligned with human values and benefits everyone fairly.
Agentic AI, by its nature, is not inherently harmful, but its capabilities demand careful oversight and responsible guidance. Developers, users, and regulators all share responsibility. Ethics shouldn’t be an afterthought; it should be built in from the start. As we build increasingly intelligent machines, we must also rise to eliminate the ethical challenges they pose.The blend of ethics and engineering prepares humans to lead this transformation well. Courses like The Ethics of AI from Davidson College on edX help you understand the ethical questions we need to ask as AI becomes more powerful. For those beginning their journey with agentic AI, free courses from Great Learning, such as Building Intelligent AI Agents and Getting Started with Agentic AI, offer practical introductions to how autonomous systems function and the challenges they pose. For learners seeking to apply these concepts in real-world contexts, Great Learning’s Postgraduate Course in Artificial Intelligence provides more in-depth training and hands-on experience. Exploring these educational resources can help build a balanced understanding of both the technical capabilities and ethical responsibilities involved in developing agentic AI. The future of AI should be built not just with innovation but with intention, and that starts with informed people.
Authored by Dr Pavankumar Gurazada, Senior Faculty, Great Learning & Adjunct Lecturer, Northwestern University
[Disclaimer: The views expressed are solely of the author and Analytics Insight does not necessarily subscribe to it. Analytics Insight shall not be responsible for any damage caused to any person/organization directly or indirectly.]