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.
Retrieval‑Augmented Generation (RAG) lets large language models first fetch relevant documents and then generate answers, addressing the inability of models to answer private or domain‑specific queries by precisely feeding them the most pertinent knowledge.
The author, a former backend engineer turned AI Agent developer, explains how LLM uncertainty, context engineering, shifting code responsibilities, workflow standards, new failure modes, and the ReAct paradigm shape modern Agent development, and outlines tasks best suited—or unsuited—for LLMs.
The article outlines a systematic Prompt Engineering process that starts with defining task goals and metrics, structures prompts into modular components, uses offline evaluation and bad‑case analysis, incorporates RAG or tools when needed, and continuously monitors accuracy, hallucination, latency and cost.
The article explains MCP (Model Context Protocol) as a standardized interface for AI applications to access external data sources and tools, compares it with function calling, outlines its security considerations, architecture, resource types, and transport options such as Stdio and Streamable HTTP.
This article breaks down the concept of AI agents for interview preparation, covering their definition, core components like planning, memory, and tool use, differences from plain LLM chats, real‑world challenges, typical use cases, detailed component analysis, and a runnable pseudo‑code example.
The article outlines a step‑by‑step learning roadmap for AI Agent development, covering large‑model fundamentals, prompt engineering, retrieval‑augmented generation, agent architecture, production practices, and fine‑tuning concepts to help engineers progress from entry‑level to senior roles.
