Everything You Need to Know About Agentic Artificial Intelligence

Artificial intelligence (AI) is probably the most revolutionary technological advancement in recent years. It wouldn’t be wrong to say that AI has redefined how we live now. Today, Internet of Things (IoT) devices and tools have become an integral part of our lives.

Not just this, AI has also made our cities smarter. However, as much as AI is involved in our lives, it’s also a fact that most people know nothing about it, particularly the agentic AI.

Agentic AI is one of the most advanced types of AI. Primarily, it focuses on maximizing the efficiency of specific tasks with the least human intervention.

Anyway, I will explain everything you need to know about agentic AI in this article. So, if you want to learn more about this fascinating type of AI, read on:

What is Agentic AI?

Traditional AI tools, such as ChatGPT, are fundamentally generative in nature. This means that a user must enter prompts to generate results. Additionally, ChatGPT can summarize research papers while also helping you brainstorm different ideas.

Point being, AI tools like ChatGPT can complete a diverse set of tasks with continuous human intervention. However, agentic AI cannot perform a range of tasks. Rather, it specializes in certain tasks and acts independently to achieve pre-determined goals with minimum human interference.

That being said, agentic AI tools are quite adaptable. They learn through interactions and take feedback to improve their response.

How Does Agentic AI Work?

Agentic AI consists of models that help tools and gadgets to mimic human thought-process while dealing with specific tasks. Though it would be wrong to assume that agentic AI perfectly mimics a human brain, such tools and gadgets can make decisions with their own logic and reasoning.

Let me explain this with an example:

Suppose you’re creating an app and ask ChatGPT to write the backend code for it. Undoubtedly, ChatGPT can ace that task, but it won’t be able to copy and run that code for you. On the flipside, an agentic AI tool can not only write the code, but it will also copy and run it autonomously.

Other than this, agentic AI tools like Microsoft CoPilot can be used to create workflows. For instance, I work as a software engineer and have created multiple autonomous workflows that significantly reduce my workload.

However, do keep in mind that agentic AI tools might not work efficiently without a top internet connection. Personally, I switched to Spectrum Internet because I found it to be reliable enough to handle multiple agentic AI tools simultaneously.

Types of Agentic AI

Based on how agentic AI models generate results, they are divided into:

• Single-agentic AI system
• Multiple-agentic AI system

A single-agentic AI system is based on one agentic AI model, which is responsible for handling all tasks. On the other hand, a multiple-agentic AI system includes several agentic AI models.

Each agentic AI model has a specific role to play, and they collaborate to break down a complex problem into small segments to resolve it. Additionally, multiple-agentic AI systems can further be divided into:

• Horizontal Multiple-Agent: Each agentic AI model has the same level of technical proficiency but specializes in a specific task. 
• Vertical Multiple-Agent: Agentic AI models follow a hierarchy where lower-level models usually solve fewer complex queries. Whereas the top AI models are responsible for resolving complex queries that require more advanced processing power and advanced large language models (LLMs).

Major Concepts in Agentic AI

Agentic AI tools and devices make decisions and learn over time. Such qualities make agentic AI quite smart, and below are several complex concepts that play a key role in its development:

• Perception: Agentic AI is autonomous, but it still needs data for training and continuous learning. APIs, large databases, sensors, or user interactions help train agentic AI models and improve their performance over time.
• Reasoning: The next step after data collection is to analyze it to extract meaningful results. Agentic AI models process the user queries and datasets with the help of natural language processing (NLP). 

Here, the goal of the agentic AI model is to find patterns and understand users’ queries to determine its course of action.

• Goal Setting: Once the agentic model obtains meaningful results from the datasets, the next step is to choose an optimum pathway to achieve the objectives.

Agentic AI follows a set of logical steps, often called a decision tree or reinforcement learning, where it experiments with different ways to reach its goal through a trial-and-error method.

• Decision-Making and Execution: In this step, an agentic AI model evaluates different courses of action after carefully considering their efficiency, user preference, and other relevant factors. Once it decides the best way to implement the user query, it executes the command autonomously.
• Learning and Adaptation: Agentic AI models actively take user feedback and learn by interacting over time. It constantly evaluates its performance and refines its responses to become more effective and useful.

Frequently Asked Questions

What is the role of NLP in agentic AI models?

Agentic AI models use NLP to understand human intent. It also plays a vital role in helping agentic AI models to mimic humans’ responses to certain situations.

How is agentic AI different than traditional AI?

Agentic AI can mimic human responses and make autonomous decisions. However, traditional AI tools cannot decide anything on their own and follow users' instructions to generate results.