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.
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.
