From idea to production for LLM apps in the EU: what actually blocked your first launch?

Suppose you have an idea for a service that turns research papers into industry-specific insights for product managers. You build a quick prototype in your AI tool of choice, show it to a coworker, and get encouraging feedback. Now you want to turn it into a service that customers will use and pay for. What has to change before you can responsibly ship it? I'm exploring this transition, with a focus on LLM app building and observability. I'm also considering developing practical training and, eventually, services around it. I'd like to understand where teams actually get stuck before deciding what to build or teach. The research service above is an example, not a claim that I've already launched it. Here is the checklist I'm trying to challenge: 1. Personal data and the EU requirements that actually apply. What personal data reaches the model, its providers, and our logs? What is the lawful basis, what can we avoid collecting, and how do we handle transparency, deletion and retention? For the AI Act, I would assess the intended use and our role rather than assume every agent is high-risk. Which obligations apply to this particular service, and what evidence do we need? 2. Useful traceability without logging everything forever. If a customer questions an answer, can we reconstruct the sources, model and prompt versions, tool calls, and relevant approvals? What should we redact or retain, for how long, and who can access it? An execution trace isn't a complete explanation of a model's internal reasoning. I'm not assuming a blanket legal requirement to archive every span or disclose everything on demand. 3. Abuse, prompt injection and business rules. A user might try to make an e-commerce agent sell a €100 item for €1, use our paid LLM access for unrelated tasks, or extract internal data. Instructions hidden in a retrieved paper or web page could also redirect the agent. How do you combine guardrails with server-side price validation, scoped permissions, tenant isolation, rate limits and spending caps? Which actions need human approval? 4. Accountability across connected systems. Once an agent accesses an ERP, CRM or database, can we connect the action to the initiating user, tenant, request and agent identity? More importantly, does the target system enforce what that identity is allowed to do? A trace ID alone cannot protect the data. 5. Quality and value after launch. How do we evaluate both safety controls and answer quality as models, prompts and data change? For the research example: are citations valid, claims supported, and findings useful? When users report a bad answer, can we distinguish retrieval failures from generation or tool failures? Do people return, save time and pay—and what do we learn from those who leave? I'd also want to measure cost per useful result, including human review, and have a fallback when the system fails. This is probably incomplete, and parts may be over-engineered for a small first release. If you've shipped an LLM application for EU customers: which ONE issue actually blocked launch or caused trouble afterwards? What happened, how did you handle it, and what remains painful? Roughly how much engineering or review time did it cost? Also interested in what you safely deferred until later. One concrete example is more useful than a complete checklist; no confidential details needed.