The UAE’s Ambitious Leap into Agentic AI
The United Arab Emirates has set an audacious course to become a global leader in artificial intelligence, with government agencies now moving beyond experimental pilots toward large-scale deployment of autonomous, agentic systems. This transition marks a fundamental shift from using AI as a support tool to positioning it as an active decision-making and execution layer within public administration. According to industry observers, the Gulf Cooperation Council (GCC) region has demonstrated exceptional commitment to AI-led transformation, yet many organizations are still grappling with the gap between strategic vision and operational execution.
Aben Pagar, head of digital risk consulting at Konexo, noted that while GCC governments have made commendable progress in setting ambitious AI strategies and investing in national capabilities, governance frameworks often remain stronger at the policy level than in day-to-day practice. “Many governance frameworks are well-articulated at a strategic level, but are still maturing in terms of how they are embedded into day-to-day operations and system design,” he said. “This gap becomes more visible as governments move beyond pilots.”
The UAE’s stated goal is to transition a significant portion of government services to autonomous, agentic AI models within the next two years. This ambition, experts argue, raises the stakes for governance and accountability. Agentic AI systems differ from previous generations because they can autonomously perform tasks, coordinate workflows, and make decisions within predefined boundaries. They have the potential to transform how governments deliver services, manage infrastructure, and support policymaking—but only if the risks are managed responsibly.
Governance as a Continuous Operational Function
Accountibility is becoming a central concern as AI adoption expands across public services. Experts argue that governance can no longer be treated as a periodic compliance exercise; it must become a continuous operational function embedded directly into systems and processes. “Each AI system should have a clearly designated owner, responsible for its performance, risks and compliance throughout its lifecycle,” Pagar said. “Decisions influenced by AI must be explainable and, where necessary, challengeable. AI is no longer simply a tool supporting decisions – it is increasingly becoming part of the decision-making layer itself.”
Nasser Ali Khasawneh, global head of technology and digital sector and global co-head of AI at Eversheds Sutherland, pointed out that GCC countries have already laid important foundations by creating dedicated AI authorities. “GCC countries have been amongst the first to create central AI bodies or ministries with a clearly defined remit over AI strategy,” he said. Khasawneh believes these institutions will play an increasingly important role as governments seek to scale AI adoption while maintaining oversight. “As this transition unfolds, governance frameworks will need to evolve accordingly,” he explained. “The government is likely to maintain and expand on its structured, risk-based implementation models, with clearer expectations on how controls are applied in practice.”
The need for robust governance is particularly acute in the context of agentic AI, which operates with a higher degree of autonomy than previous systems. Without clear lines of responsibility and mechanisms for oversight, there is a risk that autonomous systems could make decisions that are inconsistent with public policy or even harmful. The UAE’s push toward agentic AI therefore requires not just technical innovation but also institutional innovation in how accountability is designed and enforced.
The Importance of Data Governance
The shift toward agentic AI also elevates the importance of data governance. Pagar argues that data protection will form the backbone of future AI governance frameworks. “Data protection will increasingly form the backbone of AI governance, particularly around data quality, consent and cross-border considerations,” he said. “At the same time, transparency and explainability will become more important as AI begins to play a more active role in decision-making.”
Experts also point to growing concerns about cybersecurity, model governance, and data sovereignty. “Cyber risk now extends beyond infrastructure into the models themselves, including risks such as manipulation, misuse and unintended behaviour,” Pagar added. “As a result, security is becoming an integral part of AI design and governance.” Organisations are increasingly focusing on explainability, validation, and lifecycle management, while data residency requirements are influencing architecture choices, supplier selection, and deployment models.
For public sector organisations looking to move AI projects from experimentation into production, governance must be embedded directly into systems and processes. “The key is to embed governance directly into the AI lifecycle rather than treating it as a separate compliance layer,” Pagar said. “This starts with establishing clear visibility over where AI is being used across the organisation, followed by risk classification based on impact and sensitivity.”
Scaling Responsibly: From Pilots to Production
Looking ahead, experts believe the most significant public sector AI use cases will emerge in automated citizen services, regulatory supervision, intelligent case management, and smart infrastructure operations. As governments pursue increasingly autonomous systems, the challenge will be less about identifying opportunities and more about implementing them responsibly. “The ambition is clear,” said Pagar. “However, the primary challenge is not identifying use cases, but scaling them responsibly. Integration with legacy systems, maintaining transparency in decision-making, and building public trust will all be critical.”
The UAE’s push toward agentic AI is part of a broader trend across the GCC, where governments are investing heavily in digital transformation. Countries like Saudi Arabia, Qatar, and Bahrain have also established national AI strategies, but the UAE’s two-year timeline for transitioning services to autonomous models sets an aggressive pace. This ambition has drawn both praise and caution from observers.
One key aspect of scaling responsibly is ensuring that AI systems are tested thoroughly before they are deployed in critical services. Many governments have adopted sandbox approaches, allowing them to experiment with AI in controlled environments before going live. However, as Abu Dhabi and Dubai move toward broader deployment, the need for continuous monitoring and auditing becomes paramount. The UAE’s Telecommunications and Digital Government Regulatory Authority (TDRA) has been working on guidelines for AI governance, but experts say more work is needed to translate these into enforceable standards.
Cybersecurity and Model Integrity
Another dimension of the governance challenge is cybersecurity. As AI models become more deeply integrated into government operations, they also become potential targets for attack. Adversarial manipulation of AI models—such as data poisoning, model inversion, or adversarial examples—could have serious consequences if autonomous systems are making decisions about public services, infrastructure, or even security. The UAE has been proactive in building cybersecurity capabilities, but the unique risks posed by AI require new approaches to threat modeling and incident response.
Pagar emphasised that security must be built into AI design from the start, not bolted on later. “Security is becoming an integral part of AI design and governance,” he said. Organisations need to consider not only the security of the data used to train models but also the integrity of the models themselves throughout their lifecycle. This includes monitoring for drift, ensuring that models behave as expected in production, and maintaining audit trails for all decisions.
Building Public Trust
Ultimately, the success of the UAE’s agentic AI push will depend on public trust. Citizens need to feel confident that AI systems are fair, transparent, and accountable. Achieving this will require not only technical safeguards but also meaningful engagement with the public about how AI is being used and what protections are in place. The UAE has already taken steps in this direction through initiatives like the UAE AI Ethics Guidelines, but as AI becomes more pervasive, trust-building efforts will need to intensify.
Khasawneh noted that the government is likely to continue evolving its approach, learning from early implementations and adjusting regulations as needed. “As this transition unfolds, governance frameworks will need to evolve accordingly,” he said. The next few years will be critical for the UAE to demonstrate that it can scale AI innovation responsibly, setting an example for other nations to follow.
Source:ComputerWeekly.com News