Agentic AI and Future of Technology: Building Smarter, Faster and Ethical Societies

Christopher Nolan's sci-fi movie TENET tried to describe in several ways about the potential of super intelligent algorithms. As we step into 2025 today, do we get to see the algorithmic nuances of Agentic AI unfold in its full potential this year?

I have been following tech for quite sometime now, found Agentic AI one of the most exciting and transformative trends in the AI landscape. Unlike traditional AI, which reacts to specific prompts, Agentic AI can act independently, breaking down complex tasks into manageable steps, making decisions and even learning from its mistakes. It is highly developed which tends to replicate human decision-making as closely as possible.

Imagine an AI system analyzing a news piece. Instead of just processing the text, it also examines accompanying images, videos and social media conversations. This holistic approach enables a richer, more nuanced understanding of the subject. Also, doing a sentiment analysis in auto pilot while personalizing news content, and on the other hand detecting fake news.

Or imagine you’re a lawyer working on a complex case. Instead of manually reviewing hundreds of documents, you could use an agentic AI system to analyze precedents, draft clauses and simulate negotiation scenarios. This AI doesn’t just follow instructions, it adapts, plans and refines its output based on real-time feedback.

Also imagine AI chatbots analyzing not just a customer’s words but also their tone, facial expressions, and past interactions to provide better support. Visual AI could enable applications like generating realistic 3D environments from sketches or creating tailored fashion designs based on individual preferences. These advancements have already started and promise to revolutionize industries from entertainment to retail.

Artificial Intelligence is no longer confined to sci-fi movies or the labs of the top tech companies. It is now actually getting all around us and seeping in into our day-to-day workflow. It is changing the way we interact with media, simplifying tasks, transforming industries and shaping the way we live. Everyday, I come across news about groundbreaking AI developments, from more efficient algorithms to applications that can write, draw and even compose music. And behind these mind-boggling innovations lies a structured ecosystem that makes this possible which we all need to understand and draw lines for its ethical practices for a sustainable society.

To take the discusion forward, Agentic AI refers to artificial intelligence systems designed to act autonomously and take self initiative in performing tasks, making decisions and achieving goals. Unlike reactive AI, which only responds to specific inputs or instructions, agentic AI is proactive, capable of adapting to new situations, and making decisions based on its understanding of the environment. Some examples of Agentic AI are Self-Driving Cars which make decisions about navigation, traffic and safety without real-time human input, Chatbots with Decision Capabilities which can escalate customer issues to appropriate departments or solve problems autonomously, then AI in Robotics includes machines capable of performing complex tasks like assembling products or exploring in hazardous environments.

The key breakthroughs driving Agentic AI is its ability to "reflect." These systems can analyze their own actions, identify errors and improve over time. For instance, an AI writing code can test its output, find bugs and rewrite problematic sections without human intervention. While this isn’t 'self-awareness' in the human sense, but it represents a significant step toward self-improvement in machines. The term Agentic AI should not be confused with the term AI Agents.

Key characteristics of Agentic AI

Autonomy: Operates independently without constant human oversight.

Goal-Driven: Designed to pursue specific objectives or tasks.

Learning Ability: Adapts to new situations through machine learning and other AI techniques.

Decision-Making: Assesses and selects actions based on available data, often weighing trade-offs.

Proactivity: Anticipates needs or changes and acts accordingly, rather than waiting for explicit instructions.

How does Agentic AI compare to unsupervised learning?

Agentic AI and unsupervised learning are distinct yet interconnected concepts in the field of artificial intelligence, with differences in purpose, autonomy, proactivity, decision-making, and their relationship. Agentic AI refers to systems designed to act autonomously, making proactive decisions to achieve specific goals. It emphasizes agency—the ability to make decisions and take actions independently of human intervention. On the other hand, unsupervised learning is a machine learning paradigm that identifies patterns and relationships in data without requiring labeled outputs. Its primary focus is on discovering hidden structures or clustering data points, making it a tool for learning rather than autonomous action.

In terms of autonomy and learning, Agentic AI operates by relying on learned or programmed decision-making processes to function in real-world scenarios. It often integrates multiple learning paradigms, such as supervised, unsupervised, and reinforcement learning, to adapt and perform tasks. For example, a self-driving car uses environmental data to decide when to stop, turn, or accelerate. Unsupervised learning, however, is not inherently autonomous. It specializes in finding patterns or features in data and is often employed to pre-train models or provide insights that can later be used by systems like Agentic AI. An example would be an unsupervised algorithm that identifies clusters of traffic patterns without deciding how a vehicle should navigate them.

When it comes to proactivity, Agentic AI stands out as goal-oriented and proactive. It applies learned knowledge to make decisions and perform tasks actively, such as a recommendation engine that updates its suggestions dynamically based on user behavior. In contrast, unsupervised learning is passive and focused on discovery. While it excels at identifying patterns, such as clustering similar user profiles, it requires further integration with decision-making systems to act on these insights, such as selecting which content to display to users.

Agentic AI also involves a robust decision-making layer that combines learning with reasoning. For instance, a chatbot powered by Agentic AI uses real-time data to decide how to interact with users effectively. Unsupervised learning, in comparison, provides insights rather than directly making decisions. An example would be an unsupervised model grouping similar news articles without determining which one to display to a reader.

Despite these differences, unsupervised learning can play a foundational role in enabling Agentic AI. During the training phase, Agentic AI may leverage unsupervised learning to cluster data or reduce dimensionality, which enhances its understanding of the environment. In the action phase, Agentic AI uses these insights to make autonomous decisions and perform tasks, showcasing how the two paradigms can complement each other in practical applications.

Applications

Agentic AI has immense potential to transform everyday life for the common man by automating complex tasks, simplifying decision-making and enhancing productivity.

Here are some applications and how it can be useful across various aspects of daily life:

1. Personal Assistants

2. Simplifying Routine Tasks

3. Enhancing Productivity

4. Empowering Financial Decisions

5. Improving Healthcare Access

6. Supporting Small Businesses

7. Making Travel More Convenient

8. Creativity and Content Creation

9. Personalized Problem-Solving

10. Media and Marketing

11. Boosting Creativity

12. Entertainment and Leisure

13. Legal and Administrative Work

14. Accessibility and Inclusivity

15. Environmental and Social Impact

The AI Stack: The Foundation of Innovation

To understand where AI is heading, it’s essential to know where it’s coming from. Think of the AI stack as a metaphorical house that enables all AI functionalities. Like any house, it has three critical components - the foundation, the structure and the brain.

Foundation: The Hardware

The hardware is the bedrock of the AI stack. Companies like NVIDIA and Google have developed GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) that can process enormous amounts of data at breakneck speeds. These high-performance processors are the engines that power AI systems, enabling them to perform tasks like natural language processing and image recognition.

Structure: Cloud Infrastructure

Once the hardware is in place, you need the right environment to connect and scale it. That’s where cloud platforms like AWS, Azure, and Google Cloud come in. These services offer the tools, scalability, and global reach required to manage and deploy AI systems efficiently. Cloud infrastructure is what makes AI accessible—not just to tech giants but to smaller developers and researchers around the world.

Brain: Foundation Models

At the top of the AI stack are the foundation models—massive, pre-trained systems like OpenAI’s GPT-4, Google’s Gemini, and Meta’s Llama 2. These models are trained on diverse datasets, absorbing knowledge from text, images, code, and more. They serve as the "brains" of AI applications, enabling a wide range of capabilities from generating human-like text to creating hyper-realistic images.

Balancing Innovation and Responsibility

The rapid evolution of AI, from the structured AI stack to agentic, visual, and multimodal systems, is transforming the world at an unprecedented pace. However, this progress must be tempered with responsibility.

As AI becomes more powerful, we must ensure it serves humanity’s best interests, enhancing creativity, solving complex problems and improving quality of life without creating inequalities or ethical dilemmas.

The rise of agentic AI represents a pivotal moment in this journey. By giving machines the ability to plan, adapt, and learn, we are opening doors to possibilities once thought impossible. But with this power comes a shared responsibility to design, deploy and use AI systems that are not just intelligent but also ethical and fair.