The ultimate multi modal prompt: How to link visual ID to text output

I've been thinking a lot about prompt chaining and multi modal data lately. We all know LLMs are amazing with text, but they get stuck when you introduce complex real world identity, right? The key is bridging the visual gap.

I recently experimented with a specific type of AI search system. I used faceseek to audit how an external visual agent handles identity. The goal was to see if I could write a prompt that would leverage this identity tool.

Imagine this prompt chain: "Access the external face vector database (via an API like faceseek). Find the text output associated with this specific user's face (INPUT: user photo). Then, summarize that text for tone and professional intent."

This completely bypasses the PII barrier and unlocks true real world context for LLMs. The challenge is writing the prompt that can effectively integrate and analyze that biometric ID input and return useful, safe data. This isn't just text output; this is identity-aware text output. Has anyone here written or designed prompts that successfully incorporate external, specialized data agents like this? What were the ethical guardrails you had to build in?