What is AI Agent & Agentic AI

‍What Is Agentic AI?

Agentic AI, or AI Agents, are well-known concepts in the field of Artificial Intelligence now. An AI agent is a system or program capable of performing tasks autonomously using Artificial Intelligence, without requiring human intervention.

AI Agents represent the foundational form of Agentic AI, where a system or agent is designed to execute specific tasks by following a predefined workflow, operational path, and design provided by a user or human to accomplish a particular task seamlessly.

However, when environmental conditions or events change, the agent adapts by evolving and learning independently to achieve its objectives in the most efficient manner. Through continuous and rigorous learning, the AI agent becomes increasingly precise and refined over time.

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Problem Solving, Learning, and Understanding Problems: Analyzing problems involves receiving data from both external and internal environments, understanding the limitations and boundaries set by the problem.

There may be loopholes that could lead to catastrophes, losses, or inconveniences for the owner. It is essential to think from both the customer's perspective and the owner's perspective to create extraordinary delight, improve outcomes, and generate exponential revenue for the owner.

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This process is enhanced with the help of LLM (Large Language Model) models integrated into the system as a compound model. These models learn and evolve over time, making complex tasks easier and more user-friendly.

They assist users at every step by leveraging present data, past data, and external data to guide them toward the most appropriate and effective solutions.

How Does an Agentic AI Works as compare to Traditional AI ?

Agentic AI is integrating different Large Language Models (LLMs) into complex decision-making processes. When multiple LLMs are integrated together within a process, it becomes a Compound LLM model. Traditional AI agents typically utilize only past data and information, limiting their learning capabilities to the available data.

In contrast, Agentic technology leverages data, models, information, and various sub-workflows. It employs learning models to create its own paths and processes, operating more independently and reducing the need for extensive human intervention.

Decision-making and problem-solving are user-specific and can be executed in different stages to solve real-time problems within limited boundaries, while taking action independently. This process typically consists of 3 to 4 steps/stages:

‍Defining Aim and Roadmapping:While AI agents work independently and do not require human intervention, they still need a well-defined goal or aim to fulfil the user’s problem and make decisions to reach the goal. The developer must understand the user’s objectives, as they are the ones who implement the AI agent, while users are those who utilize it. Though the AI agent operates autonomously, it interacts with the user to understand their goals, then makes decisions based on internal and external environmental inputs and behaviours to deliver the best solution for the user’s needs. 

AI agents perform tasks, improve by themselves, and create sub-tasks through continuous training and learning. Through this iterative process, they ultimately reach the defined goal

‍AI agents have reasoning abilities to utilize tools. They collect data that is already available, such as external and internal environmental data, web searches, and other large database systems or APIs. AI agents don’t always possess all the knowledge required upfront; instead, they create subtasks to acquire new information and understand complex goals. As they interact with users and extract data from available sources, they continuously recheck, analyze, and reassess the data. This iterative process enables self-correction, learning, and assessment to refine their actions.

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Agentic AI VS Chat bot Application/Software?

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 Key Differences Simplified

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AI Agent (e.g., Smart Home Assistant):

1. Goal: Save energy.
2. Action: Automatically adjusts thermostat, turns off unused lights, and learns your schedule.
3. Adapts: Notices you leave early on Fridays → updates routine.
   
   

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Chatbot (e.g., Pizza Order Bot):

1. Task: Take pizza order.
2. Action: Asks "What toppings?" → Confirms order.
3. End: No further action.

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In conclusion, Agentic AI represents a transformative leap in how technology approaches problem-solving and decision-making. Unlike traditional AI, it combines autonomy, adaptability, and continuous learning to tackle complex tasks efficiently. As businesses and individuals embrace these intelligent systems, the potential for innovation, efficiency, and personalized solutions grows exponentially. The future of AI lies in its ability to not just perform tasks but to think, learn, and evolve—unlocking unprecedented possibilities for industries and society at large.