AI Agent Engineering Interviews

50 selected AI Agent engineering interview questions across 5 core categories, with 10 questions per category.
LLM Fundamentals, Context Engineering, Retrieval-Augmented Generation (RAG), Agent Design, and LLMs in Production.
Progress from core concepts to tool calling, long-term memory, retrieval evaluation, execution recovery, multi-agent collaboration, and production quality, latency, cost, and safety.

5 Chapters, 50 Problems

0% Completed

LLM Text Generation Process

LLM Tokens and Context Windows

LLM Sampling Parameters

LLM Embeddings and Semantic Similarity

LLM Hallucination and Mitigation Strategies

LLM Instruction Following Limits

Poor Fits For LLMs

Designing Tool Calling for AI Agents

LLM Model Selection Trade-offs

LLM Quality, Latency, and Cost Trade-offs

LLM Instruction Hierarchy

LLM Few-Shot Prompting

Avoiding Overlong, Fragile, and Hard-to-Maintain Prompts

What Goes Wrong When Context Is Too Much or Too Little?

Handling Long Conversations Beyond the Context Window

Stable JSON Output and Schema Design

What Validation Should Be Applied Before Model Outputs Enter Downstream Systems?

Prompt Injection vs Normal User Instructions

What Should an AI Assistant Save as Long-Term Memory?

How Should an AI Assistant Resolve Conflicts Between Memory and the Current Conversation?

What Is RAG, and Why Can't We Rely Only on a Model's Own Knowledge?

What Problems Do RAG and Fine-tuning Solve?

What Modules Does a RAG System Typically Include?

RAG Chunking Strategies for PDFs, Web Pages, Markdown, Tables, and Code Repositories

When to Use Vector Search, Keyword Search, Hybrid Search, and Rerank

How Should top-k Be Chosen in RAG, and What Goes Wrong If It Is Too Large or Too Small?

How Do You Evaluate Retrieval Quality and Answer Quality in RAG, and How Do Their Metrics Differ?

How Should Citations and Sources Be Shown in RAG Answers So Users Can Verify Them?

How Should a RAG System Handle Document Updates, Deletions, Permission Changes, and Reindexing?

How Should a Multi-Tenant RAG System Isolate Data?

What Is an Agent? How Is It Different from a Single LLM Call or Fixed Workflow?

What Is ReAct? Explain Its Principles

Plan-and-Execute vs ReAct Agent Architectures

MCP in Agent Systems vs Function Calling

When Should an Agent Continue, Stop, Fail, or Ask for Human Confirmation?

Preventing Agent Infinite Loops, Repeated Tool Calls, Runaway Costs, and Dangerous Actions

Recovering Long-Running Agents After Interruptions

When Is Multi-Agent Collaboration Appropriate, and When Is a Single Agent Better?

How Should Multi-Agent Systems Share State, Pass Context, and Merge Results?

How Should a Coding Agent Verify Its Code Changes?

What Logs and Traces Should an LLM System Record?

How Would You Debug an Agent That Keeps Calling the Wrong Tool?

How Do You Diagnose a Bad Production LLM Answer?

Timeouts, Retries, Rate Limits, Outages, and Fallbacks for LLM Requests

How Can an LLM System Give Users a Clear, Recoverable State When It Fails?

How to Reduce Latency in LLM Applications

LLM Cost Control: Prompt, Model, Cache, Batching, and Routing Optimization

When Is Caching LLM Output Dangerous?

How Should a System Detect and Respond When a Model Provider Upgrade Changes Behavior?

Safely Releasing Prompt or Model Upgrades