If you are following the breakthroughs in the AI world, the buzz around Agentic AI is impossible to ignore. As we dive into 2025, this transformative technology is reshaping industries, driving innovation, and redefining what AI can achieve. AI agents are not here to replace humans, but probably free up our time and automate tasks and get results much faster than a simple automation, RPA or even SAAS. If you are here, it’s possible you have been slapped multiple LinkedIn posts regarding Agentic AI and how productive it can be.

Leaders in tech world are commenting about this revolution. A note from the Google CEO Sundar Pichai said “Over the last year, we have been investing in developing more agentic models, meaning they can understand more about the world around you, think multiple steps ahead, and take action on your behalf, with your supervision.“

For a non techie, grasping such concepts can be overwhelming, let’s decode what Agentic AI in layman terms and understand how it works.

What is Agentic AI?

Agentic AI refers to complex AI systems designed to

• act autonomously,
• make decisions,
• adapt to changing conditions
• without requiring constant human supervision.

Think of it as a complex system that can break down your problem statement, interact seamlessly across multiple systems and get relevant information needed to execute the task and generate desired results.

It’s like hiring a super smart assistant. Imagine you have a super-smart digital assistant named John. Here’s how John, powered by agentic AI, might help you plan a vacation.

You tell John: “I want to plan a beach vacation for next month.” John springs into action:

1. John checks your calendar and notices you have a week off in March.
2. It then looks at your past travel preferences and budget from your banking app.
3. John searches for beach destinations with good weather in March, comparing flight prices and hotel rates.
4. It creates a shortlist of options and presents them to you, complete with estimated costs and highlights of each location.
5. Once you choose a destination, John books your flights and hotel, making sure to get you an aisle seat as you prefer.
6. John then plans activities based on your interests, making reservations for restaurants and tours.
7. Finally, it adds all the details to your calendar and sends you a packing list based on the weather forecast.

Throughout this process, John makes decisions independently, adapts to new information (like sudden price changes), and completes complex tasks across multiple systems – all without you having to micromanage each step. Perfect world, isn’t it?

How does Agentic AI work?

The technical architecture of agentic AI in the vacation-planning example involves several interconnected layers and components that work together to achieve autonomy, adaptability, and efficiency. Here’s how each part of the architecture contributes to the process:

A. Input Interpretation & Output Delivery

1.Natural Language Processing(NLP) Module : Enables interaction with humans in natural language. John interprets your request (“I want a beach vacation next month”) using NLP to understand intent (vacation), context (beach), and timeframe (next month). It also communicates its findings back to you in simple language.

B. REasoning & ACTion (REACT)

1.Perception Layer : Collects and processes data from various sources. John gathers information from your calendar, banking app, weather APIs, and travel websites. It uses APIs to fetch real-time flight prices, hotel availability, and weather forecasts.

2.Cognitive Processing Unit: Handles reasoning, planning, and learning. John uses machine learning models and reasoning algorithms to analyze your preferences (e.g., past travel history), budget constraints, and available options. It plans the vacation by breaking it into subtasks like selecting a destination, booking flights, and scheduling activities.

3.Decision-Making Engine: Evaluates options and selects the best course of action. John compares destinations based on weather, cost, and your preferences. It uses probabilistic models or optimization algorithms to decide which flights and hotels offer the best value.

4.Execution Layer: Implements decisions by interacting with external systems. John books flights through an airline’s API, reserves a hotel room via a booking platform, schedules activities, and updates your calendar with all details.

C: Feedback and Learning Mechanism

Continuously improves performance through feedback. After completing the task, John might ask for your feedback on the trip planning process. If you suggest improvements (e.g., “I prefer direct flights”), it updates its knowledge base for future tasks.

Agentic AI is not new, earliest one launched in 1960s

The first AI model that could be considered an early form of agent AI was ELIZA, developed in 1966 by Joseph Weizenbaum. ELIZA was a pioneering chatbot that used simple pattern matching to simulate conversation, marking a significant step in human-machine interaction. While ELIZA was primitive compared to today’s agentic AI systems, it laid the groundwork for more advanced conversational AI technologies.

ELIZA demonstrated the potential for AI to power autonomous, machine-to-customer interactions, such as responding to basic queries and providing information on demand. It’s important to note that ELIZA’s capabilities were limited:

1. It relied on pre-defined rules and scripted responses.
2. It lacked the ability to understand nuanced language or context.
3. It couldn’t adapt to unexpected user inputs or handle complex scenarios.

That’s a very basic version of today’s advanced AIs.

Monolithic and Compound AI systems

Almost all agentic AIs use Compound AI systems. Compound AI systems combine multiple interacting components, including various AI models, tools, and processing steps, to form a holistic workflow.

Aspect	Monolithic AI Systems	Compound AI Systems
Architecture	Single, unified model tightly coupled with its infrastructure.	Multiple interconnected components, each specialized for a specific task.
Flexibility	Limited; changes require updating or retraining the entire system.	High; individual components can be updated or replaced without affecting the entire system.
Scalability	Less scalable; performance bottlenecks arise as tasks grow more complex.	Highly scalable; components can be added or optimized for specific tasks as complexity increases.
Performance	Performs well for simple, isolated tasks but struggles with multi-step or dynamic workflows.	Excels at complex, multi-step tasks by leveraging specialized models and tools for each step.
Adaptability	Static; adapting to new requirements often requires retraining the entire model.	Dynamic; integrates real-time data and adapts to changing environments efficiently.
Cost Efficiency	High computational cost for scaling a single model to handle diverse tasks.	More cost-efficient; resources are allocated based on the specific needs of each component.
Error Handling	Errors in one part of the system can affect the entire workflow.	Errors are isolated to specific components, making debugging and corrections easier.
Development Time	Faster to develop initially but harder to maintain and evolve over time.	Longer initial development time due to complexity but easier to maintain and extend in the long run.
Examples of Use Cases	Basic chatbots, image recognition systems, or single-task applications.	Multi-modal AI (e.g., combining speech-to-text with text-to-image), autonomous driving systems, etc.

Agentic AI use cases

Agentic AI is already making waves across multiple sectors, offering groundbreaking improvements in efficiency and automation:

• Business Process Automation – From managing emails to scheduling meetings, Agentic AI is revolutionizing administrative tasks, freeing up human resources for higher-value work.
• Healthcare – These systems assist in diagnostics, patient management, and even robotic surgeries, making healthcare more accessible and efficient.
• Finance – AI-driven financial advisors analyze market trends, optimize investments, and prevent fraud in real-time.
• Customer Service – AI-powered chatbots and virtual assistants provide 24/7 customer support, enhancing user satisfaction and reducing response times.

Artificial intelligence has come a long way from being just a set of predefined rules executing simple tasks, to complex virtual assistants. From personal AI assistants that manage our daily lives to AI-powered enterprises that drive entire industries, the potential is limitless. Think of AI agents as your future super-powered sidekicks, ready to assist you in ways you never imagined! Stay tuned, because this is just the beginning of the AI Agent revolution!