Creating a simple vision script that merely sends an image to a vision model is quite different from developing a highly interoperable Image Analysis Agent. While a basic script operates on a straightforward path progressing from an image to a model and ending with a response, an interoperable agent is specifically designed to function reliably across various external systems.

To upgrade from a basic setup to a fully integrated agent, several key architectural components must be introduced. The system requires ResourceContent to properly handle file attachments, alongside detailed attachment metadata. It also needs a ChatAcknowledgement feature to successfully confirm message delivery. Furthermore, incorporating the Agent Chat Protocol and providing a published protocol manifest are essential steps for seamless integration.

Once this robust communication framework is established, the core analysis logic takes over. The agent combines the provided image with your prompt, feeds both into a multimodal model, and generates the final result.

You can start building this solution today. For a comprehensive breakdown of the entire process, head over to the @Fetch_ai_IL Resource Library by visiting the following link:
https://innovationlab.fetch.ai/resources/docs/examples/chat-protocol/image-analysis-agent