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Abstract This paper introduces a multi-agentic solution that leverages Generative AI—specifically, Large Language Models (LLMs) coupled with domain-specific engines—to enhance the efficiency, consistency, and technical depth of reservoir simulation workflows. The solution targets three high-value areas: simulation model compliance, insight generation, and well placement optimization, with the goal of accelerating field development planning and institutionalizing engineering best practices. The system is built around multiple AI agents, each integrating LLM-based natural language interfaces with specialized domain engines tailored for reservoir engineering tasks. The Reservoir Model Assessment Agent automates audits of simulation models by validating inputs, well constraints, and history matches internal modeling standards. The Reservoir Model Insights and Assessment Agent enables engineers to analyze, extract, and visualize critical model behaviors—such as production trends, scenario comparisons, and pressure evolution—through conversational queries. The Well Placement Optimization Agent blends simulation outputs with geospatial and operational constraints to generate ranked infill opportunities, simulate their performance impact, and provide rationale for each recommendation. All agents operate within a secure enterprise platform and are accessed via a unified, web-based interface that abstracts technical complexity while preserving engineering rigor. Applied to a synthetic reservoir with over 100 wells sourced from various publicly available sources and multi-decade production history, the multi-agentic system demonstrated substantial performance improvements. Model compliance reviews were completed in less than 20% of the time required by traditional manual methods, with the AI agent identifying more than 85% of known deviations. Insight extraction tasks that typically took 2–3 days were reduced to under an hour, while enabling deeper analyses such as recovery factor diagnostics and scenario benchmarking. The well placement agent rapidly evaluated over 25 new infill candidates, identifying zones with up to 6.5% projected ultimate recovery uplift. The domain-specific engines embedded in each agent ensured high fidelity in technical outputs, while the natural language interface enabled broader accessibility for both junior and experienced engineers. The deployment also standardized workflows across teams, reduced reliance on tacit knowledge, and improved transparency in decision-making. This work presents one of the first multi-agentic Generative AI solutions in reservoir engineering, combining the flexibility of LLMs with structured domain engines to deliver intelligent, explainable support across key simulation workflows. The result is a scalable, engineer-centric system that bridges AI and subsurface science to accelerate FDP and improve technical quality.