Agentic AI: Agents and Tools explained

Do we all agree that brain is the most critical and reasonable part of the human body but even it needs hands and legs to perform things in day-to-day life? If Yes then understanding Agents and Tools shall be nothing but a piece of cake.

Why aren’t AI models enough?

AI models have excelled in countless areas, and their progress is nothing short of amazing. But even AI has its limitations. It can solve a problem for you, but it can’t solve your problem entirely.

Sounds confusing? Let’s break it down.

Imagine you need help writing a LinkedIn post. You go to a chat model like ChatGPT, describe what you need, and it instantly generates a polished post. Seems like your problem is solved.

But what if you want to create 10–15 posts at once for better engagement? ChatGPT can still generate the content for you, but you’ll have to manually copy and publish it. Can ChatGPT itself generate and post on your behalf automatically? The answer is no. And that’s the line between what AI can and cannot do for you.

What we do MANUALLY?

Extending the same example, think about what we actually do when making a LinkedIn post.
We open LinkedIn, paste or edit the content, and then hit POST.

Behind the scenes, LinkedIn is making API calls and running functions to publish that post. But do we ever stop to monitor those API calls or worry about which functions are triggered?
Of course not. We simply use the interface, while the background details stay hidden.

What if someone was capable of doing all this for us without needing our interface.

Enter Brain a.k.a Mastermind

In our previous article, we discussed System Prompts and various Prompting Techniques. These techniques give AI the context it needs i.e. in other words, they provide the AI with a personality or role to maintain. With that, the AI can generate almost any type of content we want, solving the “what to do” part of the problem.

But that naturally leads to the next question: “How to do it?”
For example, how do we actually take that AI-generated content and post it on LinkedIn?

Tools

The literal meaning of tools is simple, objects we use to complete a task or achieve a goal.

In the context of Agentic AI and something like posting on LinkedIn, tools take on a more technical meaning. A tool could be the function that stores our content in the LinkedIn database, or an API call that actually publishes the post. In short, tools are the building blocks the AI uses to turn instructions into real actions.

Brain using Tools a.k.a Agents

We’re now very close to the endgame. Imagine if we could give the AI brain the power to use tools on our behalf. In that case, AI wouldn’t need to click buttons or navigate an interface — it could directly call the functions that get the job done.

But here’s the catch: the AI doesn’t actually “know” how those functions work under the hood. It only knows what the function is capable of and when it should be invoked to achieve the goal.

This is the leap from solving “what to do” to solving “how to do it.” And that’s where Agents come in.

Agentic AI

We know that under the hood, AI models aren’t really designed to “calculate” but designed to detect patterns in human language.

So when you say, “I want 4 LinkedIn posts on Agentic AI and I want them published,” the model doesn’t truly understand calculations or platforms. What it does recognize is the pattern of your request:

• Generate 4 articles

• Post them on LinkedIn

Now, if we’ve given the model access to the LinkedIn API and explained what each function can do, the model doesn’t need to understand how the APIs work internally. Even with just a set of if-else choices, it can decide:

• When to create content

• When to call the right function to publish

The plus point? It works faster than us, and it doesn’t need a user interface to figure out what buttons to click. It just goes straight to the action.

Conclusion: The Promise of Agentic AI

Agentic AI represents the next leap in how we work with intelligent systems. Traditional AI models could generate content, answer questions, and guide us on what to do. But with agents empowered by tools and APIs, AI can now also handle the how to do it by taking real actions on our behalf.

Think of it as moving from having a smart advisor to having a smart teammate: one that not only suggests what should be done but also goes ahead and does it and without needing an interface.

Of course, this doesn’t mean AI magically understands everything under the hood. It’s still about pattern recognition, clear instructions, and well-defined tools. But by bridging the gap between thinking and doing, Agentic AI opens the door to a future where models don’t just generate answers, they get things done.

In further blogs we would be diving into the practical implementation of these Agentic Ai with some code, until then keep coding!