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 explains what Agentic RAG is, contrasts it with ordinary RAG by detailing its dynamic decision‑making, multi‑step retrieval loop, higher cost and latency, and suitable scenarios, and outlines two implementation patterns—single‑agent and multi‑agent—plus a concise interview response.
The article explains why Retrieval‑Augmented Generation (RAG) addresses the static knowledge limitation of large models, contrasts its role of “what to say” with fine‑tuning’s focus on “how to say,” compares costs and performance against long‑context models, and offers a practical hierarchy (Prompt → RAG → LoRA/QLoRA fine‑tuning → Distillation) plus best‑practice combinations.
The article explains that the fundamental distinction between AI Agents and Workflows lies in who holds decision‑making control, outlines common Workflow patterns, describes the step‑by‑step operation of Agents, and provides clear criteria for choosing the appropriate approach in interview scenarios.
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.
