This PhD project at The University of Manchester aims to revolutionize computational fluid dynamics (CFD) by developing an Agentic AI system that automates mesh generation and synthesizes best practices from decades of CFD literature. Mesh generation and methodological best practices are major bottlenecks in CFD, often requiring expert intervention and extensive trial-and-error. The proposed research will create a multi-agent AI framework, where specialized agents handle user input, mine literature for proven strategies, evaluate mesh and solver performance, and use reinforcement learning to iteratively refine both meshing strategies and extracted best practices. The system will be built on open-source platforms such as Gmsh and OpenFOAM, ensuring transparency and reproducibility. Initial test cases will focus on canonical problems before advancing to complex flows, such as the reverse swing of a cricket ball. The reinforcement learning framework will enable adaptability, with reward functions guiding continuous improvement. Over time, the system will build a knowledge base of meshing strategies and CFD workflows that generalize across geometries and benchmark against industry standards. Expected outcomes include an open-source repository of literature-based strategies, a multi-agent AI framework for best practice synthesis and mesh automation, and demonstrated improvements in CFD workflow efficiency, reliability, and accessibility. The project is fully funded through the UKRI AI CDT program and Cummins Inc., offering home tuition fees and a tax-free stipend. Applicants should have a strong background in engineering, applied mathematics, physics, or computer science, and be motivated to bridge CFD and AI. The university encourages applications from diverse backgrounds and supports flexible study arrangements. The application process requires submission of transcripts, CV, a supporting statement, referee contact details, and an English language certificate if applicable. The deadline for applications is December 5, 2025, with a start date in September 2026.