Share Agent interview questions and standard answers, offering a one‑stop solution for Agent interviews, backed by senior AI Agent developers from leading tech firms.
This article outlines five practical principles for designing AI Agent tools—workflow‑oriented granularity, semantic naming, meaningful return values, token efficiency, and comprehensive descriptions—illustrated with concrete examples and interview‑focused guidance.
This curated collection gathers AI Agent interview questions covering fundamentals, tokenization, skill design, RAG, MCP, memory systems, evaluation methods, and practical engineering pathways, offering a complete navigation resource for backend engineers transitioning to AI roles.
The article explains AI Agents as LLM‑based entities with planning, memory, and tool‑use capabilities, covering model pre‑training, fine‑tuning, hallucinations, the Model Context Protocol (MCP), tokenization, Retrieval‑Augmented Generation (RAG), multi‑layer memory, Skill packaging, ReAct reasoning‑action loops, self‑reflection, Harness engineering, and Spec‑Driven Development (SDD).
The article explains Spec‑Driven Development (SDD), a methodology that defines requirements, system behavior, constraints, and tasks in a specification document before AI generates code, compares it with Test‑Driven Development, lists common SDD frameworks, and outlines suitable and unsuitable scenarios.
The article defines Harness Engineering—also called "驾驭工程"—as a set of engineering methods that create a structured environment for AI agents, addressing issues like missing context, tool access, feedback loops, and security, and contrasts it with prompt engineering while providing concrete implementation steps.
Evaluating AI agents requires more than accuracy; you must measure task completion, execution trace, tool usage, latency, cost, error rates, and both explicit and implicit user feedback, using observability, offline smoke‑test and regression suites, and continuous online monitoring to create a closed‑loop improvement process.
This article breaks down the fundamentals of large model training—covering data, parameters, neural networks, loss functions, gradient descent, pre‑training, and fine‑tuning—in plain language so readers can grasp how massive models learn without needing to dive into complex mathematics.
The article explains how pretraining on massive generic data creates a reusable base model, while fine‑tuning uses smaller, high‑quality task‑specific data to adapt the model, covering objectives, data scale, cost, methods, and why most projects prefer fine‑tuning.
The article outlines a five‑step process for creating robust Agent Skills, covering when to encapsulate a task, extracting decision logic and anti‑patterns, writing concise instructions, provisioning workflows and verification loops, and iterating with real‑world testing to ensure reliability.
The article explains how to split an AI agent's memory into short‑term and long‑term layers, compares fixed‑window truncation with rolling summarisation for session memory, and details building a vector‑based long‑term store, its benefits, drawbacks, and governance practices.
