The subsurface engineering industry faces a critical bottleneck as data complexity outpaces human bandwidth, causing traditional manual workflows to stall project timelines. Engineers often struggle with asynchronous simulation jobs that finish during off-hours, turning expected 24-hour turnarounds into multi-day delays due to the time required for manual data synthesis and parameter adjustments. To address this, a new approach utilizes Agentic AI integrated with NVIDIA's full-stack accelerated computing platform to create 24/7 simulation loops. This technology shifts the engineer's role from execution to strategic supervision. A central orchestration agent manages a squad of specialized sub-agents that function like a team of digital junior engineers. These agents autonomously handle repetitive tasks, monitor simulation cycles, and make data-driven decisions without human intervention. By eliminating idle time between iterations, the system ensures continuous operation, allowing for rapid scenario testing and complex optimization studies such as history matching and field development. A recent case study applied this multi-agent framework to a well-placement optimization problem using the Brugge benchmark model. The goal was to maximize net present value by adjusting 30 well locations. In the initial phases, the agents explored the solution space broadly using genetic algorithm variants. As the workflow progressed, the agents strategically pivoted to more refined methods, such as particle swarm optimization, to deepen the search. The results demonstrated faster convergence and improved resource distribution compared to traditional baseline methods. The intelligence behind these agents is powered by NVIDIA Inference Microservices (NIM), which provide the low-latency performance required for real-time reasoning. The system employs the Llama-3.3-Nemotron-Super model for complex planning and multi-turn workflows. To ensure accuracy, Retrieval-Augmented Generation (RAG) is utilized with the Llama-3.2-NeMo-Retriever model, grounding agent responses in proprietary technical documentation and simulation manuals. The architecture is modular and compatible with LangChain and LangGraph, enabling seamless function calling for simulator APIs and database queries. Deployment flexibility is a key feature, allowing users to prototype rapidly using cloud API endpoints before transitioning to secure, on-premises self-hosted models to maintain data sovereignty. This approach transforms what was once a fragmented, expert-dependent process into an automated, always-on system. By removing the cognitive bottleneck of the heuristic pause, engineers can explore a wider solution space and drive higher asset value. The framework is tool-agnostic and applicable to any industry relying on complex simulations. Open-source repositories and documentation are now available for developers to implement and customize these agentic workflows for their specific needs, offering a measurable opportunity to reduce operational costs and accelerate project delivery.