Track Agent Steps, Monitor the Action

You're building a smolagents assistant for a basketball coach who needs help analyzing PDF reports that contain player stats, scouting insights, and game strategies.

The coach relies on the agent to answer questions like: "What defensive strategies should we run against their second unit?"

But the coach doesn't just want answers — they want visibility into what the agent is doing behind the scenes.

In this exercise, you'll write an action callback that runs every time the agent takes a step, such as calling a tool or using the model. This callback will:

Show the number of steps,
And if the agent has finished, display how many tokens were used.

This will help the coach (and you!) monitor how the agent is progressing and how much work it's doing to reach a conclusion.

This exercise is part of the course

AI Agents with Hugging Face smolagents

Exercise instructions

Complete the function signature by adding the agent_step parameter.
Check whether the current step produced a final answer using the .is_final_answer attribute of agent_step.
If it's the final answer, get the total number of tokens from total_tokens and print it.

Hands-on interactive exercise

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

# Define an action callback that accepts the agent step and the agent
def action_callback(____, agent):
    step_num = agent_step.step_number
    print(f"Step {step_num}: Analyzing basketball data!")
    
    # Check if the agent step includes token usage
    if agent_step.____:
        total_tokens = agent_step.token_usage.total_tokens
        # Print how many tokens were used
        print(f"Analysis complete! Total tokens used: {____}")

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.

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