6G applications demand extreme performance, including ultra-low latency (microseconds), high reliability, and deterministic execution for services like immersive XR and autonomous systems, often powered by distributed AI/ML. Current serverless (FaaS) platforms, designed for stateless, asynchronous cloud workloads, are ill-suited. They struggle with significant cold start latency, unpredictable multi-tenancy performance, complex orchestration overheads, and limited support for stateful processing. This project proposes a novel Real-Time FaaS (RT-FaaS) paradigm to overcome these limitations. It focuses on designing deterministic, ultra-low latency inter-function data transfer leveraging technologies like DetNet/TSN; real-time, overhead-aware scheduling algorithms for dynamic workloads; scalable and secure stateful function management with predictive cold start mitigation; and an agent-based, self-optimizing control substrate that learns and adapts for holistic QoS.