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AI0-001 Implementing AI Solutions Practice Question

A company wants to build a conversational agent that can handle complex multi-step tasks such as booking a flight, reserving a hotel, and scheduling a car rental in a single session. The agent must be able to break down the user's request into sub-tasks, call external APIs, and reason about the results. Which design pattern is BEST suited for this requirement?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

An agentic workflow implementing the ReAct pattern with tool use

Agentic workflows, particularly the ReAct pattern, combine reasoning and acting (tool calls) allowing the agent to iteratively decompose tasks, use APIs, and adapt based on results.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Retrieval-Augmented Generation (RAG) with a vector store

    Why it's wrong here

    RAG retrieves static knowledge; it does not natively support multi-step tool use or external API calls.

  • An agentic workflow implementing the ReAct pattern with tool use

    Why this is correct

    ReAct (Reasoning+Acting) agents iteratively decompose tasks, call APIs, and reason about results, perfectly suiting complex multi-step workflows.

  • A single large language model prompt with all instructions

    Why it's wrong here

    A single prompt cannot dynamically break tasks into sub-steps and call multiple APIs with conditional logic.

  • Fine-tuning a model on a dataset of flight, hotel, and rental conversations

    Why it's wrong here

    Fine-tuning does not provide the ability to call external APIs or dynamically plan sub-tasks at inference time.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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