Are AI agents becoming capable faster than we're learning how to control them?

Hi guys, I've been thinking about whether we're approaching AI agent development backwards. Most of the attention right now seems to be on making agents more capable. Better reasoning. More tools. More context. More autonomy. Longer workflows. But once an agent can actually take actions instead of just generating text, I think the harder problem becomes control. For example, imagine a customer service agent that can access an account, check an order, issue a refund and update the CRM. The interesting question isn't really whether the model is capable of doing those things anymore. It's: What exactly should it be allowed to do? If refunds under $200 can be automated, what happens at $201? If the customer asks something ambiguous, who decides whether the agent should continue or escalate? What if two policies conflict? What if the agent calls the right API with the wrong parameters? And afterward, can you actually reconstruct why the agent did what it did? It feels like there's a growing gap between: Agent capability and Agent control/governance As agents become more autonomous, I think we need much better infrastructure around: permissions guardrails action limits escalation rules audit trails monitoring rollback/recovery accountability Basically, giving an agent more intelligence isn't enough. You also need to define the boundaries within which that intelligence can operate. Curious how people running agents in production are handling this. Do you build governance into the agent itself, put a separate control layer around it, or mostly rely on prompts + tool permissions? And what's been the hardest part to control once you moved from a demo to production? Please share your thoughts, thanks!