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Graded Quiz: Build Self-Improving Agents with LangGraph :Agentic AI with LangChain and LangGraph (BM RAG and Agentic AI Professional Certificate) Answers 2025

Question 1

Why are utility-based agents more advanced?

❌ They select any action that completes a goal
❌ They update behavior via trial-and-error
❌ They avoid internal models
They evaluate and rank outcomes using a utility function before selecting an action

Explanation:
Utility-based agents assess trade-offs and choose the best action, not just any goal-satisfying one.


Question 2

Advantage of a reflection loop:

It allows the AI to self-critique and iteratively improve responses
❌ Removes human oversight
❌ Prevents incorrect facts completely
❌ Reduces API calls

Explanation:
Reflection improves quality via critique → revision cycles.


Question 3

Next step in Reflexion workflow (“foods high in iron”):

❌ Generate new random response
❌ Return initial answer
❌ Start a new agent
Send response to a tool for citations, then revisor updates answer with source-backed information

Explanation:
Reflexion improves reliability by combining tools + revision steps.


Question 4

Improving health advice with citations:

❌ Manually add citations
Pass both initial output + tool data to the revisor for a citation-backed revision
❌ Restart responder
❌ Remove constraints

Explanation:
Revisor integrates authoritative references and corrects unsafe advice.


Question 5

Purpose of tool_calls in AIMessage:

❌ Log queries
❌ Run revisor’s system message
❌ Define entry/exit nodes
Store key-value outputs like answer, reflection, search queries

Explanation:
tool_calls holds structured tool outputs needed for the Reflexion cycle.


Question 6

Correct first step in ReAct:

Think: “I need to check Zurich’s weather”
❌ Recommend clothing immediately
❌ Ask for closet inventory
❌ Query clothing tool first

Explanation:
ReAct always begins with THINK → ACT → OBSERVE.


Question 7

Purpose of the “Observation” step:

❌ Log user history
Show the tool’s returned result during graph execution
❌ Display final answer
❌ Evaluate reasoning

Explanation:
Observation = tool output, which the LLM uses for the next reasoning step.


🧾 Summary Table

Q No. Correct Answer Key Concept
1 Utility evaluates ranked outcomes Utility-based agents
2 Reflection improves responses Reflection loop
3 Tool fetches citations + revise Reflexion workflow
4 Pass outputs + tools → revisor Citation-based revision
5 Stores tool outputs AIMessage.tool_calls
6 Think first ReAct pattern
7 Tool output shown for next reasoning Observation step