Hello Reader,

Almost every cloud and Gen AI interview right now includes this question. And almost every candidate gets it wrong. Not because they don't know Gen AI. But because they know too many terms and connect none of them.

Let's fix that today.

Question: What is an AI Agent?

Common but average answer - "An agent can perform complex tasks without a prompt."

Why is this average? It doesn't explain the superpower of an AI agent. It doesn't show how agents are different from a simple conditional workflow or a standalone LLM. The interviewer is left with nothing to distinguish you from the candidate before you.

The other bad answer I hear constantly - candidates throw in a bunch of Gen AI terms. MCP. RAG. A2A. Tool calls. Bedrock.

All correct terms. Zero connection to the actual question. Listing vocabulary is not an answer. The interviewer asked you what an AI agent is. Not what Gen AI terms you've heard of.

A good answer is

An AI agent is a software program that autonomously and independently chooses the best actions until a task is complete.

That last part is the key. Until task completion. Not one response. Not a pre-defined sequence of steps. The agent decides what to do, does it, evaluates the result, and keeps going on its own.

This is what separates agents from two things candidates often confuse them with:

• Standalone LLM - You send a prompt, it returns an answer. That's it. No feedback loop. No action taken. It will not go do something on your behalf.
• Conditional workflows - These follow a fixed, hardcoded sequence. If X then Y. No autonomy. No dynamic decision-making.

Let me show you with an example.

Say you send this prompt to a standalone LLM: "Identify issues in my AWS application and fix them."

The LLM gives you an answer. Maybe a good one. But it stops there. Nothing happened in your AWS environment. No logs were pulled. No fix was applied. You got text.

Now, same prompt to an AI agent.

The agentic code receives it, consults the LLM, and then starts working. It decides to first invoke a tool to pull your CloudWatch logs. It gets the logs back. It identifies an error pattern. It then searches documentation to understand what might be causing those errors. It finds the culprit. It calls another tool to apply a fix. Then it returns - "Task complete. Issues fixed."

That back-and-forth feedback loop - the LLM dynamically deciding which tools to call, in what order, how many times, that is what makes it an agent. A conditional workflow can't do that. A standalone LLM can't do that.

But if you want to really delight the interviewer, mention the implementation.

After your explanation, add this:

"Fun fact - I've actually built a proof of concept on this. I deployed the agentic code on AWS Strands, used an LLM hosted on Amazon Bedrock, and connected to tools using MCP."

Now you've gone from definition to architecture to hands-on in 60 seconds. That's not a candidate who Googled the answer. That's someone who built it.

Most candidates will define the agent. The candidate who defines it, demonstrates it with an example, and then grounds it in real implementation? That candidate pulls ahead every time.

Make sure to knock this one out of the park 🙌

Question for you readers : Has this question come up in your recent interviews? What answer did you give? Reply and let me know!

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Keep learning and keep rocking 🚀,

Raj