If you’ve built agentic AI systems, you know the exact feeling I’m talking about. The agent looks impressive in demos — it reasons step by step, selects tools intelligently, and completes complex multi-step tasks. But the moment you move it to staging or production, unexpected things start happening. It might pull extra customer records, attempt to update database fields it shouldn’t touch, or draft an email with sensitive information.I experienced this firsthand last year while leading a sales operations agent project. The agent was supposed to query CRM data, enrich leads, and suggest follow-up actions. One day we discovered it had accessed additional PII fields and nearly sent external emails. Our system prompts and internal governance documentation failed to stop it. That incident forced us to invest serious time building a proper runtime control plane — and it became a game-changer for subsequent projects.