šŸ§  AI Agents Interview Questions

Master AI agent concepts, frameworks, orchestration, and building autonomous systems

What are AI Agents and how do they differ from traditional chatbots?

Easy

AI Agents are autonomous systems that can perceive their environment, make decisions, and take actions to achieve goals. They go beyond simple question-answering.

Key Differences:

Traditional Chatbots

  • Follow predefined scripts
  • Rule-based or simple ML
  • Reactive (respond to inputs)
  • Limited context
  • No tool use

AI Agents

  • Autonomous decision-making
  • LLM-powered reasoning
  • Proactive (plan and execute)
  • Long-term memory
  • Use tools and APIs

Agent Components:

  • Perception: Understand environment through inputs
  • Reasoning: LLM-based decision making
  • Planning: Break down goals into steps
  • Memory: Store and retrieve context
  • Action: Use tools to interact with world
  • Learning: Improve from feedback
Python

Implement a ReAct (Reasoning + Acting) agent from scratch

Hard

ReAct is a powerful agent pattern that combines reasoning (thinking) with acting (using tools). The agent iteratively thinks about what to do, takes action, and observes results.

Python

Build an agent using LangChain or LangGraph framework

Medium

LangChain and LangGraph are popular frameworks for building AI agents with pre-built components and orchestration capabilities.

Python

Design an autonomous agent system with planning and memory

Hard

Advanced agents require sophisticated planning, memory management, and self-reflection capabilities to handle complex tasks autonomously.

Python

Interview Tips for AI Agents