FinTech companies across the UAE are accelerating their adoption of Agentic AI, leveraging its capabilities to tackle increasingly complex business challenges and enhance operational efficiency. This shift was highlighted during a recent industry event hosted at the FinTech Hive within the DIFC Innovation Hub in Dubai.
The event brought together leading voices from across the region’s financial technology ecosystem to explore how Agentic AI is transitioning from experimental pilots into real-world financial applications.
From Pilot Projects to Real-World Deployment
Held under the theme “From Pilot to Production: Building the Foundations for Agentic AI in MENA FinTech,” the gathering featured experts from JetBrains MENA, Tabby, and Google for Developers.
Speakers emphasized the technical and operational challenges of scaling Agentic AI, including modern engineering workflows, rigorous testing frameworks, and the shift from traditional developer assistance tools to supervised autonomous systems.
UAE Strengthens Its AI Leadership
The discussions come at a time when the UAE continues to position itself as a global hub for artificial intelligence adoption. Government-backed frameworks are accelerating AI deployment across both public and private sectors.
According to the latest Dubai Financial Services Authority (DFSA) survey, more than half of licensed firms within DIFC are already using AI technologies—up from one-third the previous year. Meanwhile, generative AI adoption has nearly tripled over the same period, reinforcing the growing importance of Agentic AI in financial services.
Human Oversight Remains Critical
Industry leaders agreed that while Agentic AI introduces powerful automation capabilities, its success depends on more than advanced models. It requires strong developer tools, robust software infrastructure, and, most importantly, continuous human oversight.
Nadia Rinsky, Head of MENA GTM at JetBrains, noted that accountability cannot be transferred to machines. She stressed that developers remain ultimately responsible for system performance, particularly in critical financial environments.
The Evolving Role of Developers
As Agentic AI becomes more prevalent, the role of developers is rapidly evolving. Rather than focusing solely on writing code, developers are now responsible for orchestrating, supervising, and validating teams of intelligent AI agents.
JetBrains highlighted its platform, JetBrains Central, as a key enabler for managing AI-driven workflows. The platform provides centralized governance, enabling organizations to enforce policies, track costs, and ensure transparency across development processes.
Scaling AI from Experiments to Enterprise
JetBrains also shared its framework for helping organizations move from fragmented AI experiments to structured, enterprise-wide capabilities. The company emphasized that centralized governance is not a limitation, but rather a critical safety mechanism for scaling Agentic AI sustainably.
Real-World Applications in FinTech
Tabby presented a practical use case demonstrating how AI agents are deployed within one of the region’s largest financial services and shopping applications. The company showcased how Agentic AI can drive operational efficiency while maintaining strict compliance with financial regulations.
Denis Sakhnoc, Head of AI Agent Platform at Tabby, highlighted the importance of focusing on operational workflows, AI-driven customer solutions, and the software development lifecycle. He explained that AI agents can support anti-fraud systems, Know Your Customer (KYC) processes, and customer service automation, provided that human validation remains in place.
Building Scalable AI Infrastructure
Majid Jamaah, Head of Cloud & DevOps at Beyond AI and a Google Developer expert, outlined the infrastructure required to deploy Agentic AI at scale. He emphasized the need for resilient architectures capable of handling variability in workloads, data traffic, costs, and latency.
He also advised organizations to begin with simple implementations and gradually scale based on real-world usage insights, ensuring sustainable growth and system reliability.
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