Agentic AI Framework for Technical Excellence: A Discipline-Based, Scalable Multimodal Assistant for Subsurface

Abstract Objective This paper introduces an AI-powered framework designed to enhance business and technical decision-making, knowledge sharing, and technical advisory. Unlike generic assistants, the system uses discipline-specific agents with contextual awareness of technical literature, company procedures, best practices, and petrotechnical software. The approach adheres to the C7 framework, which guides the development, management, and governance of complex, intelligent systems. By offering scalable, intelligent support tailored to technical domains, the framework empowers Subject Matter Experts (SMEs) and transforms how engineers interact with knowledge and operational data. The resulting system contributes to improvements at the process, business, and technical levels. Methods The proposed agentic framework is based on three pillars: AI-Subject Matter Experts: Agents with discipline-specific knowledge in narrow domains such as waterflood, thermodynamics, or petrophysics.Field Insights: Agents maintain in-depth contextual knowledge of specific fields, including technical databooks, reservoir performance reviews, field development plans, and historical lessons learned. This knowledge is continuously updated through a learning loop.Mastery of Petro-technical Software: Agents demonstrate deep understanding of reservoir simulation, including input-output logic, workflows, and troubleshooting. Equipped with skills such as retrieval-augmented generation, code writing and execution, and summarization, these agents operate collaboratively in a multi-agent environment to simulate SME collaboration and escalate queries from simple retrieval to advanced diagnostics. The architecture is model agnostic, leveraging Large Language Models(LLMs) and data connectors to internal sources. Results The agentic system has shown impressive performance across three tiers of value delivery: Efficient Retrieval: Enables rapid access to validate, structured knowledge for engineers and non-specialists.Advisory Support: Provides diagnostic and interpretive assistance, reducing dependence on isolated expertise.Integrated Responses: Delivers scenario-based support by combining technical references, field parameters, and procedural logic, enabled by multi-agent coordination. Adhering to the C7 framework (Command, Control, Coordination, Communication, Computing, Correctness, and Compliance), the system aligns with enterprise standards, enabling organized operations, effective teamwork, reliable results, and policy adherence. Evaluation includes ground testing and SME review. Pilot deployments in Waterflood, Field A, and Reservoir Simulation have demonstrated up to 60% time savings, faster onboarding, and more consistent decisions. Novel/Additive Information Implementation uses curated company data and discipline-specific agents, integrating human expertise for continuous improvement. The system is refined through the updating of documentation, workflows, and prompts, fostering communities of practice, and facilitating expert consultations. Its architecture enables collaborative reasoning and the integration of technical literature, procedures, and multimodal data, with scalability across disciplines and platforms. This approach delivers process innovation by transforming engineering workflows, revolutionizes business decision-making through human-agent collaboration, and advances technical design with agent correctness, advanced reasoning, and multimodal content. It marks the beginning of scalable, extensible reasoning agents that can evolve with future AI capabilities. This work presents a discipline-based, orchestrated, and model-agnostic AI framework, leveraging curated knowledge, human-in-the-loop, and scalable architecture for technical excellence in subsurface engineering."