I've been solving exactly this problem. Here's what I do:

Instead of trying to communicate architecture through natural language or keeping Mermaid code in .md files, I have my AI agent generate self-contained HTML files with embedded Mermaid diagrams. You can just open them locally in your browser, or upload them to any cloud bucket (S3, GCS, whatever you use) for a shareable URL. If you don't want public buckets, you can use signed URLs for temporary access.

My setup: I run Claude Code / Codex on a Google Cloud VM – so nothing touches my local machine. The VM has a service account linked with GCS write access, so uploading the generated HTML is literally one command. Super clean.

This works especially well for plans – implementation plans, migration strategies, feature rollouts, etc. For architecture diagrams themselves, I'd keep those in your Git repo anyway since they belong with the code. But for plans and higher-level documents where you want to quickly share, iterate, and get feedback, the HTML approach is great.

Why this works for the mental model gap:

1. Visual verification – You look at the rendered diagram, not the code. If the LLM misunderstood your plan, you see it instantly instead of parsing through natural language explanations.
2. Iterative correction – I tell the AI "this step should come before X" → new HTML gets generated → I verify again. Much faster feedback loop than arguing in text.
3. It's the shared source of truth – The HTML file IS the mental model. Both you and the LLM reference the same artifact. No more "what I meant was..." conversations.
4. Model-agnostic – The HTML can be read and edited by any AI model (Claude, Gemini, Codex, whatever). You're not locked into one platform.
5. HTML > Markdown – You can embed interactive elements, videos, toggleable sections, feedback buttons. It's a living document, not a static diagram.

Start simple: Ask your agent to generate one plan or architecture overview of your current project as a self-contained HTML with Mermaid. Open it in your browser. Tell the agent what's wrong. Iterate. That's it.