We added an enforcement layer to our AI agents in production — here's what we learned about the failure modes nobody talks about

After shipping AI agents into real production environments, the failures that actually kept us up at night weren't hallucinations or bad outputs — they were **control failures.** Three things that surprised us: **1. Prompt injection is more common than you think** It doesn't require a sophisticated attacker. A malformed user input, a poisoned document in a RAG pipeline, a rogue tool response — any of these can redirect your agent's behavior. And if there's no enforcement layer, it executes. **2. "We'll add governance later" doesn't work** Compliance teams don't care that you were moving fast. When they ask *"show me every action this agent took on customer data in the last 90 days"* — you either have a cryptographically signed audit trail or you don't. There's no retrofitting that. **3. Kill switches need to be fast** When something goes wrong in production, you don't want to SSH into a server. You need org-wide agent shutdown in under 15ms. We learned this the hard way. The pattern that actually worked for us: treating enforcement as infrastructure, not an afterthought. A gate *before* execution — not a log *after* it. Curious if others building production agents have hit similar issues. How are you handling policy enforcement and audit trails today? *(We built something for this — happy to share in the comments)*