The Abhay Perspective

There is a word that keeps coming up in offices, WhatsApp groups, and headlines: automation.

Some people are excited about it. Many are confused. A large number quietly worry that it might replace their jobs.

In this article, we are going to break down what automation actually is, how it has evolved, and the massive difference between traditional automation, AI-powered automation, and the newest kid on the block: Agentic AI.

What Is Automation, Really?

Simply put, automation is getting a machine or system to do repeatable tasks without needing a human every time.

In real life, we see examples of this everywhere. An ATM automates the job of a bank cashier. A washing machine automates the physical labour of cleaning clothes.

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When we talk about software, we usually refer to Robotic Process Automation (RPA). You have likely encountered this without knowing it. It is the technology behind IVR menus that route your calls or the bots that send you emails with pre-filled templates.

The Old Way: Rule-Based Automation (RPA)

Until very recently — perhaps up to 2024 — when we talked about automation, we were talking about rules.

Traditional RPA is entirely rule-based. For an RPA bot to work, you have to write clear, rigid instructions. It follows those steps exactly. If anything changes in the structure — like a website button moving or a form field changing name — the bot fails immediately. It has “hands” to click and type, but it has no “brain” to adjust.

The New Way: AI Agents

In 2025, the conversation has shifted. We are now looking for AI Agents.

These are goal-oriented bots. You do not need to code a full algorithm or give them step-by-step instructions. AI Agents can think for themselves and use tools you give them access to in order to achieve a goal.

Technically, these agents are derivatives of Generative AI models. While standalone models like ChatGPT, Gemini, or Grok can generate text and help you be productive, they are limited. They cannot usually take action outside the chat window.

An AI Agent sits on top of these models. It uses the “intelligence” of the LLM to think, and then it goes out and executes the task without you holding its hand.

The Coffee Example: RPA vs AI Agent

Let’s understand this difference with a simple scenario.

Imagine you have an assistant whose job is to get your daily coffee from the nearby Starbucks. He must go to the same store at exactly 9:00 AM, order a tall Americano takeaway, and put it on your desk.

Meet “Roy” (The RPA Bot)

Roy is very efficient. Without fail, he follows the steps and delivers the same quality service every day. But one day, he reaches Starbucks at 9:00 AM and finds the shop is closed for renovations.

Roy panics. He does not know what to do because his instructions only said “Go to this store.” He returns empty-handed. The automation failed because the environment changed.

Meet “Clark” (The AI Agent)

Clark has also been told to get you a tall Americano by 9:30 AM. He follows the same path as Roy. However, when he finds the shop closed, he doesn’t panic.

Instead, he checks Google Maps to find the next nearest Starbucks, walks there, and gets your coffee.

The Difference

Roy (RPA) needed exact instructions and failed when variables changed.

Clark (AI Agent) only needed a goal. It understood the outcome you wanted and had the intelligence to figure out a new path when the first one was blocked.

The Future: Agentic AI

We are currently transitioning from simple task-bots to intelligent interns. However, standard AI Agents still often require a human trigger.

If you use an AI Agent to manage email, you might still need to ask it to “check my inbox” or set it on a rigid timer. This is helpful, but it is not the fully autonomous workflow companies dream of.

Enter Agentic AI

Agentic AI is the “holy grail” of automation. It is not just a single bot, but often a collective system involving:

a team, a manager, memory, and autonomy.

Let’s go back to the email example and see how each automation approach works:

• RPA Approach: You run a scheduled script every hour. It checks for new emails. If an email matches a specific keyword, it sends a templated reply. If the keyword is missing, it does nothing.
• AI Agent Approach: You ask the agent to check your mail. It reads the context, understands the sentiment, and drafts a smart reply for you to approve or send.
• Agentic AI Approach: Once deployed, this system runs continuously in the background. It detects a new email the moment it arrives. It reads it, classifies it, and decides what to do. It might reply, archive it, or even open your calendar app to schedule a meeting based on the email content.

Crucially, it has memory. If it makes a mistake and you give it feedback, it remembers that preference for next time without you needing to update any code.

It is effectively a smart digital employee that works autonomously to achieve high-level objectives.

Will This Take My Job?

It’s a genuine fear, and it’s not completely unfounded. Roles built entirely on repetitive tasks will get automated sooner or later. But this is not new. Every major technology wave has come with fears: industrial machines, computers, the internet, smartphones — yet humanity kept evolving.

The ATM didn’t end bank tellers; it changed what tellers do.Excel didn’t kill accountants; it killed arithmetic drudgery and created financial modelling careers.

Agentic systems will kill repetitive tasks, not jobs.

We already have started seeing new requirements like:

• AI Workflow Designer
• AI Reviewer/Approver
• Agentic AI Supervisor
• AI Operations Coordinator

In short, someone still has to tell the machines what “good” looks like, catch the rare mistakes, and design the bigger picture. That someone is going to be human — just a lot more leveraged.

The Bottom Line

Understand these three layers — RPA, AI Agents, Agentic AI — because in the next 3–5 years every company in India (from startups to banks to manufacturing giants) will pick one of them to get work done.

The world is moving toward AI-powered workflows.You can either watch it happen or learn how to use it to your advantage. The smarter you are at spotting which of your daily tasks belong to which layer, the faster you’ll move from:

“person whose job can be automated” → “person who automates everyone else’s job.”

That’s the game now — and I’m here to help you win it.

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