Divide and Conquer: Creating Specialist Agents

You're helping your younger sister apply to computer science programs. It's a lot to manage: researching schools, comparing programs, and writing strong essays.

To make the process easier, you decide to build two specialized agents:

One to research universities and their requirements
One to help write compelling application essays

You have access to the CodeAgent and WebSearchTool classes, as well as a pre-configured model.

This exercise is part of the course

AI Agents with Hugging Face smolagents

Exercise instructions

In the first agent, set the tools parameter to include the WebSearchTool.
Still in the first agent, provide a name string to identify it as the school research agent.
In the second agent, set the model parameter to use the provided model variable.
Finally, complete the second agent's description to summarize its purpose.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# School research specialist
school_agent = CodeAgent(
    # Assign a list of tools the agent can use
    ____=[WebSearchTool()],
    model=model,
    # Set the agent's unique name identifier
    ____="school_research_agent",
    description="Expert in researching universities, programs, and admission requirements"
)

# Essay writing specialist  
essay_agent = CodeAgent(
    tools=[WebSearchTool()],
    # Provide the model used to generate responses
    ____=model,
    name="essay_writing_agent",
    # Write a short description of the agent's area of expertise
    ____="Expert in crafting compelling college application essays and personal statements"
)

Edit and Run Code

This exercise is part of the course

AI Agents with Hugging Face smolagents

AdvancedSkill Level

5.0+

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Discover what makes code agents special and how they use Python to reason and act. Build your first agent with smolagents, add built-in and community tools for web access, and create custom tools to connect agents with data.

Exercise 1: Introduction to Hugging Face smolagents Exercise 2: Let the Agent Do the Math: Expense Insights Exercise 3: Wait, You Didn't Write Any Functions?Exercise 4: Creating an Agent With Tools Exercise 5: Writing Code Through Prompts: Compound Interest Exercise 6: Let Agents Search the Web Exercise 7: Creating an Agent With Custom Tools Exercise 8: Just Add @tool: Writing a Custom Tool Exercise 9: Check, Please! Giving Agents Access to Data Exercise 10: One Prompt, Many Tools

Transform your traditional RAG pipeline into an agentic system that retrieves information iteratively and reasons across multiple steps. Build stateful tools to support advanced retrieval, guide agents with planning intervals to improve outcomes, and use callbacks to track and customize agent behavior at runtime.

Exercise 1: RAG Refresher Exercise 2: RAG in Order: From Manuals to Answers Exercise 3: So, What’s Actually Happening in RAG?Exercise 4: The Step That Adds Meaning Exercise 5: Introduction to Agentic RAG Exercise 6: Stateful by Design: Build a RAG Search Tool Exercise 7: Connect the Tool, Unlock Agentic RAG Exercise 8: Working With Multi-Step Agents Exercise 9: Track Agent Steps, Monitor the Action Exercise 10: Game Time: Run the Full Agent

Tackle complex workflows by orchestrating teams of specialized agents under a coordinating manager. Add memory to retain context across interactions, debug agent behavior using execution traces and reasoning steps, and implement robust validation strategies to ensure high-quality, reliable responses.

Exercise 1: Introduction to Multi-Agent Systems Exercise 2: Divide and Conquer: Creating Specialist Agents

Current Exercise

Exercise 3: Meet the Team: Agent Coordination Exercise 4: Managing Agent Memory Exercise 5: Don't Forget: Keeping Memory Between Calls Exercise 6: Tracing the Agent’s Code Execution Exercise 7: Agent Final Answer Validation Exercise 8: Say More, Agent: Enforcing Answers Length Exercise 9: Smarter Agents: Validate and Retry Exercise 10: Congratulations!