Title: Quiz 2 — Tool Calling, ReAct, and Agent Planning Patterns
Course: AIINFRA 300: Agentic AI & the Model Context Protocol (MCP) > Section: Week 02 — Tool Calling, ReAct, and Agent Planning Patterns

Q: In the ReAct loop, what is the correct order of the repeating cycle?
Feedback: Sample/expected answer: Thought → Observation

Q: Classic ReAct agents build a complete plan for the entire task before taking their first action.
- True
- False
A: False

Q: According to the original ReAct paper (Yao et al., 2022), what was one measured benefit of ReAct over chain-of-thought prompting alone?
- It eliminated the need for any tool use
- It reduced hallucination and error propagation on multi-hop QA benchmarks like HotpotQA and FEVER
- It required no reasoning traces at all
- It only worked on single-step tasks
A: It reduced hallucination and error propagation on multi-hop QA benchmarks like HotpotQA and FEVER

Q: In the modern tool-calling loop, what should your code do when the model's response contains no tool calls?
Feedback: Sample/expected answer: Return the model's response directly to the user — the loop ends there; no tools need to be executed.

Q: What does setting strict: true in OpenAI's structured outputs require regarding a tool's JSON Schema?
- All properties may be optional and additionalProperties may be true
- additionalProperties must be false, and all properties must appear in required (using nullable for optional fields)
- Only string-type parameters are allowed
- The schema must be written by hand and cannot come from Pydantic or Zod
A: additionalProperties must be false, and all properties must appear in required (using nullable for optional fields)

Q: Giving an agent as many tools as possible (30+) generally improves its ability to pick the right one for any task.
- True
- False
A: False

Q: A team needs an agent to execute a long, well-understood multi-step task as cheaply as possible in tokens, without needing to react to surprises mid-task. Which planning pattern best fits?
- Classic ReAct
- ReWOO (plan then execute)
- Tree-of-Thoughts
- Reflexion
A: ReWOO (plan then execute)

Q: Why does LangChain now recommend building ReAct-style agents in LangGraph rather than using the legacy AgentExecutor class?
Feedback: Sample/expected answer: AgentExecutor hard-codes much of the agent loop's behavior and is difficult to customize, while LangGraph lets you define the model-call and tool-execution nodes and edges explicitly, giving full control over the loop.