Function Calling Vs Agentic AI

Artificial intelligence is evolving faster than ever, with tools like ChatGPT, Google Gemini, and Claude making waves. There’s a lot of buzz around function calling and Agentic AI, but what do these terms mean? How are they different? And more importantly, when should we use one over the other?

Let’s try to understand this using as simple terms as possible (For more, navigate here.)

What is function calling?

In simple terms, Function calling allows an AI model like OpenAI, Claude, or Gemini to call a predefined function to fetch or do something. You tell the AI what functions are available, and it decides which one to use based on the user’s query.\

Scenario:

A customer walks into a hardware store and asks, “Can I get a hammer?”

The store assistant knows exactly where the hammers are and brings one. That’s a function calling a single, specific request with a straightforward answer.

Example: Function calling in Action:

Let’s take an example of a weather app:

def get_weather(city):
    # code that fetches weather from an API
    return f"The weather in {city} is 24 degree celcius and potential rain."

Now you expose this function to your model using its function-calling system.

• User: What is the weather in Delhi today?

• AI: calls get_weather(“Delhi”).

• Result: The weather in Delhi is 29 degrees Celsius and potential for rain.

It’s Simple, Precise, and One-Step execution.

What is Agentic AI?

Agentic AI goes a step further. It’s not just calling one function, its thinking, planning, and taking actions in sequence. It acts like an agent that:

• Understands a problem.

• Breaks it into steps.

• Decides the order.

• Uses tools (APIs, functions, even web browsing)

• Monitors outcomes

• Adapts if something goes wrong.

Scenario:

You are planning a birthday party. The AI doesn’t just order the cake, you ask it to handle the whole party.

• It finds the right cake vendor

• Orders decorations

• Books a DJ

• Sends out invites

• Even rescheduled if the venue isn’t available.

The Agentic AI, a goal-driven AI with memory, context, and adaptability.

Example: Agentic AI in Action

Use a framework like LangChain, CrewAI or AutoGen.

from crewai import Agent, Task, Crew
from langchain.chat_models import ChatOpenAI


researcher = Agent(
    role="AI Researcher",
    goal="Research the latest AI trends in 2025",
    backstory="Expert in tech blogs an AI development",
    verbose=True,
    llm=ChatOpenAI(model='gpt-4o')
)

writer = Agent(
    role="Blog writer",
    goal="Write a compelling blog post from research notes",
    backstory="Expert in creating engaging content",
    verbose=True,
    llm=ChatOpenAI(model='gpt-4o')
)

task1 = Task(
    description="Find 3-4 latest AI trends in 2025. Include details like companies, applications, etc.",
    agent=researcher
)

task2 = Task(
    description="Find 3-4 latest AI trends in 2025. Include details like companies, applications, etc.",
    agent=researcher
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[task1, task2],
    verbose=True
)

result = crew.kickof()
print(result)

What is happening in the above code?

• Notice that you are not giving the instructions on how to use the tool.

• OpenAI automatically maps user intent to the right function.

• This is true agentic behaviour, not just calling a tool, but making decisions and collaborating.

Real-world use cases:

Function calling use cases:

• Chatbots fetching flight prices

• AI apps showing weather or stock prices

• Healthcare assistant returning BMI or test results

• Finance bots calculating mortgage or ROI

Agentic AI Use Cases:

• AI Research Assistant: Reads, summarises, and compares academic papers.

• DevOps Agent: Detects server issues, checks logs, applies fixes, and reports back.

• Sales Outreach Bot: Drafts email, sends follow-ups, and schedules meetings.

• Business Process Automation: Handles order processing, email responses, and CRM updates.

Overview:

As AI continues to evolve, the real power will come from combining both:

• Use function calling as a tool.

• Use Agentic AI as the manager that knows when and how to use them.

In your next AI project, ask yourself:

• “Do I need a tool or an intelligent agent?”

If you are looking for something a little more in-depth about the topic, navigate here.

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Thanks for coming this far in the article. I hope I was able to explain the concept well enough. If you face any issues, please feel free to reach out to me; I'd be happy to help.

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