Image Agents

• Key points are in the visual diagram above.

Much of the world's information is visual — product photos, charts, medical scans, UI screenshots. Image agents let you build workflows that see and reason about pictures, not just text.

An image agent is a visual inspector who examines photos and tells you what's wrong with a product, what a chart means, or what's on a screen.

Scenario

Quality control agent receives factory camera photo → vision model identifies scratch on surface → logs defect with bounding box → triggers rework ticket.

Solution

In Voice & Multimodal Agents, apply Image Agents to this scenario: Quality control agent receives factory camera photo → vision model identifies scratch on surface → logs defect with bounding box → triggers rework ticket. 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 Image 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 Image Agents happens in the code.

1response = client.chat.completions.create(  # call the API2    model="gpt-4o",3    messages=[{4        "role": "user",5        "content": [6            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64}"}},  # key line for Image Agents7            {"type": "text", "text": "List any visible defects."},8        ],9    }],10)

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

Quick recap — the most important points from this module.