Hello Reader,

Six months ago, an interviewer asking about Gen AI was a bonus round. Today it is standard. At AWS, Microsoft, Meta, JP Morgan, Verizon, and most large enterprise technology teams, Gen AI questions are showing up in every SA, FDE, and AI engineer interview regardless of the role's primary focus.

The candidates who answer these well are not the ones who have read the most blog posts. They are the ones who can connect the concepts to real architecture decisions and explain the trade-offs without getting lost in jargon.

Here are the questions coming up most often right now and the answers that actually land.

What is an AI agent?

The average answer

Agents can perform complex tasks with prompts. Then mention random Gen AI terms like MCP, LLM, Tool.

Why average?

This answer doesn't have the depth. And you need to correlate the terms in regards to AI Agent. Think of it like this - if interviewer asks you "What is a microservice?", and you just say the name of AWS services like EC2, Lambda, ALB, APi Gateway, that won't be a good answer

Good answer

An AI agent is a software process that autonomously and independently makes decisions and takes actions until task completion. Standalone large language model cannot take action and cannot go back and forth till the task is done.

Then you mention an example. So let me take an example. Let's say I have AI agent for troubleshooting. I can send a prompt like identify issues in my AWS application and fix them. And to fix this, the agent may need to go to different places, logs of the application, infrastructure or database, some metrics, etc.

And it happens using agentic code interacting with large language model and a bunch of tools. And you don't have to code or specify how many times a tool needs to be called. It's all happening automatically. And once the issue is found, this agentic code using a tool can also go fix it.

Now regarding the components of AI agent which ties to the next question. So you can add part from the next answer to this answer as well to impress the interviewer.

What are the components of AI Agent (Also asked as : Give me a sample AI Agent Architecture)

AI agent has the following components:

• The large language model (LLM) and that could be hosted in something like Amazon bedrock.
• Then we have the ever important agentic framework or sometimes called agent harness which is the actual code. It could be written in something like strands agent or langchain, langraph with your language of choice and this will run in Amazon Bedrock AgentCore runtime.
• This code needs access to tools and that will be done via MCP. One practical implementation of this is Amazon AgentCore Gateway.
• Say this part to delight the interviewer - Now all modern agents give you the capability of resuming your session and that is done via memory. So this strand agent code has also access to short-term memory and then a memory extraction modules periodically extracts information and puts it into long-term memory and all these could be implemented using AgentCore memory.
• In addition, we will implement security and observability to ensure AI agent is acting appropriately

So this question and the previous question is kind of overlapping so you can use them accordingly.

How would you self-host an AI agent including the LLM?

This question is getting more popular fast, driven largely by companies worried about sending proprietary data to a model provider.

Good answer:

The agent code itself is straightforward. Containerize it, push it to Amazon ECR, and run it as a pod.

The model is the hard part. A model has two pieces: the model image, containing the tokenizer and configuration, and the model weights, which for an 80 billion parameter model means 80 billion floating point numbers the prompt runs through.

Delightful answer:

Say the above part and then, walk through the full stack.

• Model weights live in S3 and run on EC2 with Nvidia GPUs, or on Inferentia using compiled models for Neuron cores.
• vLLM virtualizes access to the model so it can scale under load, the same way a hypervisor virtualizes a bare metal instance into multiple EC2 instances.
• MCP servers run via FastMCP for tools.
• Memory runs on an open-source vector database like Milvus, backed by object storage on a persistent volume.
• Karpenter and horizontal pod autoscaler handle scaling the whole thing.

The balance:

Full self-hosting buys you security and control over your data. It costs you real operational complexity across every layer, from GPU provisioning to vector database management.

You do not have to go all in either way. Plenty of teams mix and match, self-hosting models while hosting memory through AgentCore, depending on what they actually need to have more control over.

If you get these questions, make sure to crush it! I will cover more Gen AI questions in future editions.

Keep learning and keep rocking 🚀,

Raj

P.S - If you want to get an AWS Solutions Architect job without coding or learning every AWS service, the 9th cohort for AWS SA Bootcamp is launching on Aug 1st, 12 PM ET (Eastern Time) via live workshop. This program now includes our updated GenAI curriculum. Please register below:

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