Comprehensive AI Agent Interview Guide: From Core Concepts to Engineering Implementation

This article compiles a set of interview questions and reference articles about AI Agents, organized to help readers master both theory and practice.

A Single Article Explains Core AI Agent Concepts: From Token, Skill, and RAG to MCP, SDD, and Harness Engineering

ByteDance Interviewer: Stop Letting AI Write Code Directly, Learn SDD Specification-Driven Development

Interviewer Asks: What Is Harness Engineering? Here’s How to Answer

Interviewer Asks: How to Evaluate an Agent? Don’t Just Say “Check Accuracy”

ByteDance Interviewer: Explain Large Model Training Principles Without Complex Formulas – A Plain Language Overview

ByteDance Interviewer: What’s the Difference Between Large Model Pretraining and Fine-Tuning?

ByteDance Interviewer: How Are Skills Developed in Your Agent System and How to Ensure High Quality?

ByteDance Interviewer: What Is an Agent’s Memory System? Designing Short‑Term and Long‑Term Memory Solutions

ByteDance Interviewer: What Is Mixed‑Expert Mode (MOA) and Why Does It Boost Performance?

ByteDance Interviewer: What Exactly Is a Token and Which Tokenization Algorithms Exist? An In‑Depth Explanation

ByteDance Interviewer: How to Optimize RAG System Performance? From Evaluation Metrics to Optimization Techniques

ByteDance Interviewer: How to Assess a RAG System? What Evaluation Metrics Did Your Project Use?

ByteDance Interviewer: What Is the Working Principle of ReAct?

Interviewer: What Is Multi‑Agent and What Are Its Operating Modes?

Interviewer: What Are Skills and How Do They Work?

Interviewer: What Is RAG and Why Must Large Models Retrieve Information Before Answering?

Interviewer: How Did You Implement Prompt Engineering in Your Project?

ByteDance Interviewer: How Do You Understand the MCP Protocol?

Interviewer: What Is the Essence of an Agent? A Comprehensive Explanation

Top‑Tier Company Agent Engineer’s Tested Learning Path: From Beginner to Senior Role