Medicine is undergoing a paradigm shift from artificial intelligence (AI) as a passive analytical tool to AI as an active, semi-autonomous teammate. This transition is fueled by the convergence of frontier large language models (LLMs) with open-source agentic frameworks and standardized protocols such as the Model Context Protocol (MCP), which enable systems capable of complex reasoning, multi-step task execution, and real-world interaction with clinical environments. As of early 2026, multi-agent AI systems are demonstrating the capacity to transform oncology practice: augmenting multidisciplinary tumor boards with decision quality comparable to expert panels, compressing drug discovery timelines from years to months, and more than doubling clinical trial matching recall. The evidence is now substantial: an autonomous agent leveraging GPT-4 with multimodal precision oncology tools achieved 91% accuracy in clinical decision-making compared to 30.3% for the base model alone (Nature Cancer, 2025), while the MDAgents framework achieved best performance in seven of ten medical benchmarks at NeurIPS 2024. In this editorial, we examine the current evidence base and project the trajectory of agentic AI in oncology through 2028. We hypothesize that by the end of this decade, the integration of AI teammates—orchestrating complex care pathways, designing novel therapeutics, and simulating their effects in patient-specific digital twins—will begin to yield measurable improvements in patient outcomes, potentially establishing a pathway toward a 70% 10-year disease-free survival in advanced triple-negative breast cancer. The implications for clinical practice, regulatory frameworks, and the identity of the oncologist are profound.