What should teams ask before trusting an AI agent in real workflows?

A lot of AI agent demos look impressive now, but the real questions start after the demo. If an agent is touching code, customer data, workflows, tickets, docs, or internal systems, I don’t think “it worked once” is enough. Here are the questions I’d want answered before trusting it: Will I get the same output twice for the same prompt? What proves the task is actually done? Where is the ROI on the AI spend? Why did the agent make this change? Does it learn from mistakes or repeat them? What is the rollback path if something breaks? If I cancel, what do I lose? Who is accountable when things go wrong? What is correctness measured against? Does it know “done” versus just “working”? Who owns the institutional knowledge it creates? For people here building or using agents: which of these actually matters most in production, and what would you add?