Agentic AI and OpenAI SDK

Building the Future with Agentic AI and OpenAI SDK

Artificial Intelligence has entered a new era — the Agentic AI Era.
It’s no longer about static models generating text or code.
Today’s AI can reason, plan, and act autonomously — thanks to the power of Agentic AI frameworks combined with the OpenAI SDK.

What is Agentic AI?

Agentic AI refers to intelligent systems that can make decisions, execute actions, and interact with environments without continuous human intervention.
Unlike traditional AI models that only respond to prompts, Agentic AI can:

Understand context and goals

Plan a sequence of actions

Use tools and APIs to achieve results

Continuously learn from interactions

In short, it’s the move from “AI as a tool” to “AI as a teammate.”

Why Use OpenAI SDK for Agentic AI?

The OpenAI SDK provides a clean and flexible interface to build agentic workflows.
It allows developers to easily integrate GPT models, function calling, retrieval systems, and stateful agents in just a few lines of code.

Here’s what makes the SDK so powerful:

Unified API access – Call GPT-4o, Whisper, and other models from a single interface.

Function calling – Connect AI agents with your own APIs or tools.

Memory and state – Build persistent agents that “remember” past context.

Realtime capabilities – Create voice-enabled or streaming-based agents.

Creating a Simple Agent with OpenAI SDK(python)

agent = Agent(
name="Agent",
instructions="You are given the mathemetics speacialist",
)

async def main():

rating = await Runner.run(
    starting_agent=agent, 
    input="write table of 2 ", 
    run_config=config
)
print(rating.final_output)

asyncio.run(main())

How It Works

Agent Creation

agent = Agent(
name="Agent",
instructions="You are a mathematics specialist",
)

This defines an AI agent with a clear identity and specific expertise — in this case, mathematics.

Runner.run()

rating = await Runner.run(
starting_agent=agent,
input="write table of 2",
run_config=config
)

The Runner manages the execution lifecycle of the agent.
It sends your input to the agent, manages reasoning steps, and produces the final output.

Final Output

print(rating.final_output)

This displays the agent’s response — in this example, the multiplication table of 2.

Why It’s Impressive

The agent acts independently, understanding the request and generating accurate answers.

You can change its role (e.g., “science tutor”, “finance advisor”, or “code debugger”) just by updating its instructions.

The Runner automatically handles async operations, making it efficient and scalable.

Try Expanding the Agent

You can extend this example to build:

A Math Tutor Bot that solves step-by-step equations

A C*oding Assistant Agent* that writes and reviews code

A Research Agent that reads documents and explains summaries

Just change the instructions — the agent adapts instantly.

Final Thought

This small snippet is a glimpse into the future of Agentic AI —
where agents don’t just answer, but understand roles, plan actions, and deliver results through reasoning.

“With OpenAI SDK, every developer can now build intelligent agents — not just chatbots.”