Audio Agents

Not all audio is speech. Meeting recordings have tone and pauses, factory floors have machine sounds, call centers have hold music and background noise. Audio agents understand the full sonic context.

An audio agent is a sound engineer who listens to a recording and tells you not just what was said, but who spoke, the mood, and background events.

Scenario

Sales call agent transcribes 30-min call, diarizes rep vs customer, extracts objections and action items, scores sentiment per segment, logs CRM note.

Solution

In Voice & Multimodal Agents, apply Audio Agents to this scenario: Sales call agent transcribes 30-min call, diarizes rep vs customer, extracts objections and action items, scores sentiment per segment, logs CRM note. 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 Audio Agents (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 Audio Agents happens in the code.

1# Audio Agents — 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 Audio Agents8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain audio agents clearly."},11        {"role": "user", "content": f"What is audio agents?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Treating Audio Agents as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for audio agents

Quick recap — the most important points from this module.