AI-agent–enhanced knowledge graphs and memory-augmented models as a new paradigm for intelligent sintering systems en en Sintering processes play a critical role in materials manufacturing; however, their optimization remains highly dependent on empirical knowledge, fragmented datasets, and costly experimental trials. Existing modeling and machine learning approaches often lack a unified structure for representing complex relationships among processing parameters, microstructural evolution, and final material properties. This perspective article argues that knowledge graphs can serve as a missing semantic layer for organizing sintering-related data, enabling structured representation of process–property relationships across heterogeneous databases. Furthermore, the integration of autonomous AI agents equipped with memory-augmented learning models is proposed as a promising direction for continuously constructing, updating, and reasoning over such knowledge graphs. By combining structured knowledge representation with adaptive learning and agent-based optimization, this framework has the potential to transform sintering research into a self-improving, data-driven ecosystem. This perspective highlights future research directions toward intelligent, explainable, and autonomous sintering systems for advanced materials engineering. 306 310 Pouria Dianati Souha Department of Aeronautical Engineering, Faculty of Aviation and Space Science University of Kyrenia, Kyrenia, Mersin 10 Turkey puryadianati@gmail.com Knowledge graphs AI agents Memory-augmented models Sintering Intelligent manufacturing Materials informatics Vol 5 No 4 Paper 4.pdf [1] R.M. German, Sintering trajectories: description on how density, surface area, and grain size change, JOM. 68 (2016) 878–884. https://doi.org/10.1007/s11837-015-1795-8. ##[2] M. Yi, W. Wang, M. Xue, Q. Gong, B.X. Xu, Modeling and simulation of sintering process across scales, Arch. Comput. Methods Eng. 30 (2023) 3325–3358. https://doi.org/10.1007/s11831-023-09905-0. ##[3] K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh, Machine learning for molecular and materials science, Nature. 559 (2018) 547–555. https://doi.org/10.1038/s41586-018-0337-2. ##[4] S.R. Kalidindi, M. De Graef, Materials data science: Current status and future outlook, Annu. Rev. Mater. Res. 45 (2015) 171–193. https://doi.org/10.1146/annurev-matsci-070214-020844. ##[5] G. Pilania, Machine learning in materials science: From explainable predictions to autonomous design, Comput. Mater. Sci. 193 (2021) 110360. https://doi.org/10.1016/j.commatsci.2021.110360. ##[6] F. Yan, X. Zhang, C. Yang, B. Hu, J. Wang, Data-driven modelling methods in sintering process: Current research status and perspectives, Can. J. Chem. Eng. 101 (2023) 4506–4522. https://doi.org/10.1002/cjce.24790. ##[7] M.J. Buehler, Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning, Mach. Learn: Sci. Technol. 5 (2024) 035061. https://doi.org/10.1088/2632-2153/ad7228. ##[8] J.H. Montoya, K.T. Winther, R.A. Flores, T. Bligaard, J.S. Hummelshøj, M. Aykol, Autonomous intelligent agents for accelerated materials discovery, Chem. Sci. 11 (2020) 8517–8532. https://doi.org/10.1039/d0sc01101k. ##[9] A.G. Kusne, H. Yu, C. Wu, H. Zhang, J. Hattrick-Simpers, et al., On-the-fly closed-loop materials discovery via Bayesian active learning, Nat. Commun. 11 (2020) 5966. https://doi.org/10.1038/s41467-020-19597-w. ##[10] T. Lookman, P.V. Balachandran, D. Xue, R. Yuan, Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design, npj Comput. Mater. 5 (2019) 21. https://doi.org/10.1038/s41524-019-0153-8.