When to use which reasoning pattern in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Choosing the right reasoning pattern for a step-by-step problem

You want an AI agent to solve a complex math problem by breaking it down into smaller steps. Which reasoning pattern should you choose?

A Chain of Thought reasoning, because it guides the agent to think step-by-step.

B ReAct reasoning, because it combines reasoning with actions like searching the web.

C Self-Consistency, because it samples multiple answers and picks the most common one.

D Tree of Thought, because it explores multiple reasoning paths in parallel.

💡 Hint

Think about which pattern helps the agent explain each step clearly.

Chain of Thought reasoning is best for step-by-step explanations. It helps the agent break down complex problems into smaller, understandable steps.

❓ Model Choice

intermediate

Best reasoning pattern for combining reasoning and external actions

You want an AI agent that can both think through a problem and perform actions like searching the internet or calling APIs. Which reasoning pattern fits best?

A Self-Consistency, which samples multiple reasoning paths internally.

B Chain of Thought reasoning, focusing only on internal reasoning steps.

C ReAct reasoning, which interleaves reasoning and actions.

D Tree of Thought, which explores multiple reasoning paths but does not perform actions.

💡 Hint

Look for the pattern that mixes thinking and doing.

ReAct reasoning allows the agent to alternate between reasoning and taking external actions, making it suitable for tasks requiring interaction with the environment.

❓ Metrics

advanced

Evaluating Self-Consistency reasoning effectiveness

You run Self-Consistency reasoning by sampling multiple answers from an AI model and selecting the most frequent answer. Which metric best measures if this approach improved answer reliability?

A Accuracy, because it shows how often the final answer matches the true answer.

B Loss, because it measures the model's prediction error during training.

C F1 Score, because it balances precision and recall for classification.

D Perplexity, because it measures how surprised the model is by the data.

💡 Hint

Think about which metric directly shows correctness of final answers.

Accuracy measures how often the chosen answer matches the correct one, making it the best metric to evaluate Self-Consistency reasoning effectiveness.

🔧 Debug

advanced

Why does Tree of Thought reasoning take longer to run?

You implemented Tree of Thought reasoning but notice it takes much longer than Chain of Thought. What is the main reason?

A Tree of Thought uses fewer reasoning steps, so it is slower per step.

B Tree of Thought explores multiple reasoning paths in parallel, increasing computation time.

C Tree of Thought skips important steps, causing delays.

D Tree of Thought only samples one answer, so it is slower to converge.

💡 Hint

Consider how exploring many options affects speed.

Tree of Thought explores many possible reasoning paths simultaneously, which requires more computation and thus takes longer.

🧠 Conceptual

expert

Selecting reasoning pattern for uncertain or ambiguous problems

You face a problem where the AI model's answers vary widely and uncertainty is high. Which reasoning pattern helps improve answer reliability by considering multiple diverse reasoning paths?

A Chain of Thought, because it focuses on a single reasoning path step-by-step.

B ReAct, because it mixes reasoning with external actions.

C Self-Consistency, because it samples multiple answers and picks the most frequent one.

D Tree of Thought, because it explores multiple reasoning paths and selects the best.

💡 Hint

Think about which pattern explores many paths to find the best answer.

Tree of Thought explores multiple reasoning paths in parallel and selects the best, helping reduce uncertainty and improve reliability in ambiguous problems.