Most enterprises have already invested in AI for their contact centre. Most are stuck in the mid-60% on automation rates. Not because the implementation was wrong, but because the architecture was. Omilia makes the case that contact centre AI plateaus by design when it relies on manual tuning, vendor dependency, and general-purpose language models not built for synchronous, high-volume voice. True agentic AI doesn't wait to be retrained - it improves itself. The alternative is an agentic, self-learning platform that monitors every interaction, identifies what to improve, builds better agents automatically, and deploys them with human approval - compounding performance without costly re-engagement. We'll cover what this looks like at enterprise scale, how to deploy LLMs where they genuinely add value, and why specialised voice AI, not general-purpose models, is the right engine for live customer calls. We discuss what the Glass Box approach means for compliance teams who need to audit every automated decision. ROI in three months. Deployment in two weeks. Resolution that doesn't plateau - it compounds.