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Getting Started with Building AI Agents

· 7 min read
Dinesh Gopal
Dinesh Gopal
Technology Leader, AI Enthusiast and Practitioner

Welcome to the The Agentic Advantage​

We live in a world where we’re still speculating about who the next James Bond (Agent 007) might be. While we may not know who the next agent is, we’re increasingly confident about what the next generation of applications will look like: agentic applications.

A couple of years ago, everything revolved around building Retrieval-Augmented Generation (RAG) applications. Today, with rapid advancements in large language models and tooling, almost every company is talking about agents—or as some like to say, that agent life.

In a recent discussion, the Y Combinator team predicted that there will eventually be an agent for almost every SaaS application. That’s a bold claim—but if you’ve been watching how software is evolving, it doesn’t sound that far-fetched.

Starting a New Series: The Agentic Advantage​

It’s been a while since I last published a blog post—and that’s something I want to correct.

I’m starting a new series called “The Agentic Advantage”, where I’ll explore:

  • Core agent concepts
  • Agentic design patterns
  • Practical proofs of concept (POCs)
  • Lessons learned moving from demos to production

If you’d like to catch up on my earlier posts, you can find them here.

Before we start building agents, it’s important to clearly understand what an agent is not.

What Is Not an AI Agent​

Any system that performs a task once, end-to-end, in a single pass is not an agent.

For example:

PROMPT = """
Write me an essay of 100 words about different types of coffee beans.
"""

This is a one-shot prompt. The system receives an instruction, generates an output, and stops. There is:

  • No planning
  • No iteration
  • No reflection
  • No autonomy

This is useful—but it’s not agentic.

So, What Is an AI Agent?​

An AI agent is autonomous. It can:

  • Break down a high-level objective into smaller tasks
  • Decide how to execute those tasks
  • Use tools or APIs when needed
  • Reflect on outcomes and improve its results

In simpler terms:

Agents don’t just respond — they decide, act, and adapt.

Unlike a traditional RAG application that responds directly to a user query, an agent:

  • Understands context
  • Plans a course of action
  • Executes within defined boundaries
  • Iterates until it reaches the goal

A fully independent agent can:

  • Determine the steps needed
  • Identify and use tools
  • Iterate without continuous human intervention