AI Agents

116 questions

Multi-step LLM systems. ReAct, Plan-and-Execute, memory, tool use, planning, reflection, multi-agent orchestration, trajectory evaluation.

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Questions

Predict: what per-step accuracy is required to achieve 90% end to end success on a 10-step agent task?
Predict OutputHard·Qual 4.0
Browser agents drive a real UI instead of calling text APIs. What extra failure modes show up?
Short AnswerHard·Qual 4.0Anthropic
Which of the following are failure modes unique to browser agents that do not occur with structured API tool calling?
Multi-selectMedium·Qual 4.0Anthropic
How does agentic RAG differ from single shot RAG, and what does putting retrieval inside the loop enable?
Short AnswerMedium·Qual 4.0Databricks
What does agentic RAG uniquely enable compared to naive single shot RAG?
Multiple ChoiceMedium·Qual 4.0
What signal tells you to use a fixed LCEL chain instead of an agent loop?
Short AnswerMedium·Qual 4.0Anthropic
When should you use a fixed DAG pipeline instead of a dynamic agent loop?
Multiple ChoiceMedium·Qual 4.0
How does least privilege tool scoping reduce security risk in an agent system?
Short AnswerHard·Qual 4.0Anthropic
Which practices reflect the principle of least privilege applied to an agent's tool set?
Multi-selectMedium·Qual 4.0
How does assigning a persona to each agent in a multi-agent system shape its behavior?
Short AnswerMedium·Qual 4.0
Why is scoring only the final answer a weak way to evaluate agents?
Short AnswerHard·Qual 4.0Databricks
What does trajectory evaluation measure that final answer accuracy alone cannot capture?
Multiple ChoiceMedium·Qual 4.0Databricks
How does MCP differ from OpenAI-style function calling?
FlashcardMedium·Qual 4.5
What is the Model Context Protocol (MCP) and what problem does it solve?
Multiple ChoiceEasy·Qual 4.6
Describe the typical agent execution loop.
Short AnswerMedium·Qual 4.4
What distinguishes an AI agent from a plain LLM call?
Multiple ChoiceEasy·Qual 4.5

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