Distinguished Professor of Cybersecurity & Artificial Intelligence, Dr. Adnan Akhunzada, is a globally recognized leader ranked among the Top 2% of Scientists worldwide by Stanford University and Elsevier. With over 17 years of international research and academic leadership experience across Europe, the UK, the Middle East, and South Asia, he has amassed a prolific publication record of over 230 peer-reviewed works, including numerous high-impact IEEE Transactions and journal articles, with an h-index of 42 and a cumulative impact factor exceeding 800. His research leadership is underscored by his role as Principal Investigator and lead architect on competitive international funding exceeding USD 13.4M, including major EU Horizon 2020 and Vinnova projects, and he holds a granted US patent in post-quantum cryptography. Currently serving as Professor & Chair of Applied Research & Innovation at the University of Doha for Science & Technology in Qatar, his strategic academic leadership has driven institutional transformation through ABET accreditation leadership, curriculum innovation, and the establishment of advanced research frameworks. His career spans prestigious roles, including Visiting Professor at Malardalen University in Sweden, Senior Lecturer at Glasgow Caledonian University, and technical lead at the Technical University of Denmark and RISE Research Institute of Sweden, where he forged deep industrial collaborations with tech giants like ABB, Ericsson, and Volvo. A dedicated educator, Dr. Akhunzada has shaped curricula and mentored over 40 graduate students, drawing on advanced teaching certifications from Harvard University and the University of Bradford. His extensive research expertise encompasses cybersecurity, AI, machine learning, post-quantum cryptography, and secure distributed systems, while his professional service includes senior memberships in IEEE and ACM, editorial roles for leading journals, and advisory positions on national ICT policy

Title: The Sentient Stack: Hypergraph Knowledge Fabrics for Self-Evolving, AI-Native Cloud Architectures Abstract:By 2030, the dominant paradigm of software architecture will not be microservices, serverless, or even platform engineering. It will be sentient architectures, cloud-native systems that perceive, reason about, and restructure their own topology in response to changing requirements, data patterns, and operational conditions.This presentation introduces the Hypergraph Knowledge Fabric (HKF) framework, a radical departure from static architecture-as-code. Rather than treating software components as fixed nodes in a service mesh, HKF represents entire cloud ecosystems as dynamic, multi-relational hypergraphs where compute units, data streams, model inference endpoints, and business logic exist as semantic entities in a continuously evolving knowledge structure. Within this fabric, autonomous AI agents act as "architecture neurons" , reading telemetry, interpreting intent via LLMs, and rewriting deployment topologies in real time without human intervention.We demonstrate how this confluence of hypergraph knowledge representation, multi-agent reinforcement learning, and federated cloud orchestration enables systems that are not merely scalable, but self-scheming: capable of spawning new microservices when they detect emergent data patterns, collapsing redundant infrastructure before costs accrue, and migrating model inference across edge-to-cloud continua based on real-time latency-reward trade-offs. Drawing from live experiments on Kubernetes-based AI pipelines, we show that HKF-driven architectures reduce mean-time-to-adaptation by 78% compared to GitOps-driven platform engineering, while maintaining formal verifiability of structural changes through hypergraph constraint satisfaction.The talk concludes with a roadmap for the post-manual architecture era, outlining the engineering, ethical, and governance challenges of delegating structural authority to autonomous systems, and why the cloud of 2030 will be less a platform we manage, and more an organism we cultivate.Keywords: Hypergraph architectures, autonomous cloud systems, AI-native infrastructure, multi-agent orchestration, self-evolving software, federated inference, sentient architectures.