What breaks when AI agents move from demos to production?

A lot of AI agent demos focus on whether the agent can complete a task once. But once agents start touching real systems, the harder question is not only "can it do the task?" It becomes: What happens if the run fails halfway through? Which actions already happened? Which tool calls are safe to retry? Who approves risky steps? What counts as state when the agent resumes? How do you explain what happened to an operator, auditor, or customer? For simple workflows, logs may be enough. But for production-changing actions, I think the system needs something closer to an operational control plane: receipts for side effects, approval history, idempotency keys, stop/retry/compensate policy, and a human-readable view of what happened. The tricky part is that explainability cannot only mean "explain the model's internal reasoning." In many cases, especially when the agent does something unintended, that may be impossible or not useful enough. The more useful version may be operational explainability: what data the agent saw, what policy checks ran, what tools were available, what changed externally, who approved it, and what the next safe action should be. Curious how people are handling this in practice. Are you building this as internal infrastructure, relying on existing workflow tools, or just keeping agents away from production-changing actions for now?