Hooks

• Key points are in the visual diagram above.

Policy that lives only in the prompt is ignored under pressure.

Git hooks, but for the agent.

Scenario

Scenario (Claude Agent SDK): A user triggers a workflow that depends on Hooks. Hooks run before and after tool calls: log, block, rewrite args, or inject extra context. Your implementation handles the request, logs the step for observability, validates the output, and returns a grounded response — e.g. cutting manual work from 20 minutes to under 30 seconds.

Solution

In Claude Agent SDK, apply Hooks to this scenario: Scenario (Claude Agent SDK): A user triggers a workflow that depends on Hooks. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Hooks (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Hooks happens in the code.

1# Hooks — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for Hooks8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain hooks clearly."},11        {"role": "user", "content": f"What is hooks?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for Hooks before production
• No logging or tracing around claude hooks steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.