Real biology—especially immunology and translational oncology—is fundamentally stochastic, non-Markovian, and constantly evolving, making traditional static or linear pipeline approaches highly prone to error. This paper introduces a multi-agent, state-space architecture designed to model volatile biological behavior and bridge the gap to deterministic software through the Bio-Stochastic Continuum (BSC) Engine. By utilizing Non-Parametric Bayesian Swarms that calculate dynamic probability distributions in real time, our methodology moves past standard Large Language Models (LLMs) to rigorously quantify uncertainty across messy Real-World Evidence (RWE). We deploy a competitive multi-agent choreography featuring a “Clinician Proxy Agent” and a “Biochemist Proxy Agent” operating in an active feedback loop to model cellular adaptation to immunotherapies, predicting T-cell exhaustion and resistance mechanisms over time. In baseline simulation testing of a synthetic cohort of 10,000 non-small cell lung cancer (NSCLC) patient profiles, the engine successfully predicted immunotherapeutic resistance pathways an average of 22 days before traditional clinical biomarkers registered any observable change. To ensure system stability under high data volatility and eliminate telemetry cascade failures, we implemented a hard Non-Parametric Bayesian Throttling Gate (acting as a ≥ 95% confidence filter). Under an artificial 400% stress surge, the modified engine maintained 100% uptime, accelerated continuous processing latency to < 45 ms (representing a 62.5% faster execution speed), automated multi-agent mapping to reduce clinical data-ingestion friction by 84%, and achieved a 41% reduction in total compute expenditure through logarithmic stabilization. These results demonstrate the viability of using self-correcting multi-agent stochastics to translate clinical observations into high-fidelity biological simulations.