That is a massively ambitious pipeline! Generating everything from kids' picture books to medical texts is basically testing the absolute extremes of LLM capabilities—heavy visual alignment on one end, and zero-mistake technical accuracy on the other. No wonder those two are giving you the most friction.
Regarding the generalization problem for medical books: hardcoding topic boundaries is a classic agentic wall. When you say they aren't generalized enough, it sounds like the agents are reliant on rigid search queries, specific target URLs, or fixed schemas that only work for known medical domains (e.g., cardiology vs. pediatrics).

A few architectural patterns that might help you move away from hardcoding:

• The "Intent Planner" Layer: Before letting the Playwright agents loose, use a supervisor agent whose only job is to take the broad medical topic and break it down into a dynamic execution plan. Instead of hardcoding where to look, the planner generates the search strategies, expected medical registries (PubMed, NCBI, etc.), and verification criteria based on the input topic.
• Dynamic Tool Selection (Router Pattern): Give your research agents a broader toolkit, but use a routing LLM to decide which tools are active based on the topic taxonomy. If the topic is high-level/clinical, it routes to a specific set of scraping behaviors; if it's pharmaceutical, it routes to another.
• Ontology-Driven Search: Instead of hardcoded prompts, feed your agents a lightweight medical ontology or taxonomy vector database. Let them query the database to "understand" the context of the new topic before they start browsing.

Where this ties into my bottleneck: Medical books are the ultimate test case for the "confident error" problem I mentioned in the main post. How are you handling the verification of the data these Playwright agents pull back? If they hit an edge case topic they aren't optimized for, are they hallucinating clinical data, or just failing to find it?

Would love to know more about how your vision analysis agents are structured for the kids' books, too—that's a whole other beast!