Put what you have learned in this course into practice with the structured exercise below. Working through it end-to-end is the single best way to consolidate the concepts.

Exercise: Ai Function Calling in the Real World

Pick one realistic scenario from your own work, study, or curiosity and apply the techniques covered in earlier lessons. Aim for a small, completable artifact — a notebook, a working script, a written design, or a side-by-side comparison.

Suggested Workflow

1. Frame the goal: write a one-sentence problem statement that the techniques in this course can address.
2. Pick the right tool: revisit the lessons above and choose the specific method, model, or pattern that best fits.
3. Build an MVP: produce the simplest version that proves the approach works. Keep it small.
4. Evaluate: how did it perform? What surprised you? What broke?
5. Iterate: refine one decision (prompt, parameter, tool) and re-evaluate.

Self-Check Questions

• Could you explain the chosen approach to a teammate in under two minutes?
• Where might this break in production? What guardrails would you add?
• What is the next thing you would try if you had another day?

Stretch Goal

Document your run as a short write-up. Compare against alternative approaches and note the trade-offs.

This practice loop converts passive reading into durable skill — the exact transfer-of-learning step most learners skip.

Concrete Starter Project: Build a Customer Support Agent with Tool Calling

Goal: Build a customer support agent that can look up order status, process refunds, and create support tickets using function calling.

Steps:

1. Define three functions: lookup_order(order_id), process_refund(order_id, reason), create_ticket(subject, description, priority).
2. Create mock implementations that return realistic data from an in-memory object.
3. Implement the agent loop: call GPT-4o with tools, execute tool calls, return results, repeat until end_turn.
4. Set max_rounds=5 and detect repeated tool calls with a hash set.
5. Test with scenarios: "Where is my order #1234?", "I want a refund for order #5678", "My package is damaged — I need help".

Extend it:

• Add Zod validation for all tool call arguments.
• Mark process_refund as requiring human approval — print a confirmation prompt.
• Log all tool calls to a CSV file and analyze which tools are called most frequently.
• Add a 6th round that asks the agent to summarize what actions it took.

What you will learn: The complete function calling lifecycle — schema design, agent loop implementation, error handling, and safety controls for production tool use.