The Importance of Explainable AI in the Era of Agentic Systems

Explainable AI (XAI) makes AI systems transparent, explainable, and credible. It ensures users understand why a given model arrived at a specific decision so that it is easier to verify, debug, and trust the system. Let's look at all that XAI entails followed by a real-world example of how it works.

- Dr Abhishek Bandyopadhyay and Dibyendu Banerjee

Agentic AI represents the next evolution of intelligent systems—digital entities that not only follow instructions but also set goals, make decisions, and learn independently. These agents will soon encompass every aspect of human life, from finance and energy to robotics and personalised courses. But with this exciting potential comes a critical challenge: understanding what these agents are thinking.

Agentic AI does not simply follow conventional systems but engages in self-directed behaviour influenced by constantly changing goals, interaction with the environment, and acquired knowledge, thereby giving rise to some very complicated and often opaque processes behind decisions. An example: A totally autonomous vehicle may suddenly make an unexpected manoeuvre. Or a healthcare agent may recommend some treatment that is not of common standard protocol. Certainly, knowing what an AI agent has done is insufficient. We want to know why it did so.

But why is this understanding so vital? First, we are more likely to trust technologies we understand. If agentic systems are black boxes making decisions without clear rationale, it will lead to erosion of public trust and subsequently hinder the spread of such systems, and the benefits derived from them. Would you trust a rationally opaque entity to control your health or finances?

Second, it is something that must be done. Since agents are going to take on new roles, accountability is primary. If an autonomous agent inflicts damage or makes a blunder, who comes under fire when there is no knowledge of the system’s mental processes?