As organisations across the Middle East accelerate investments in artificial intelligence (AI), attention is rapidly shifting from generative AI (GenAI) pilots to a new frontier: agentic AI systems capable of taking autonomous actions, making decisions and orchestrating workflows.
But while interest is growing, many enterprises are discovering that AI performance depends less on models and more on the quality, accessibility and governance of enterprise data.
Against this backdrop, Denodo has announced a series of integrations with Amazon Web Services (AWS) designed to create trusted data foundations for agentic AI deployments across hybrid and multi-cloud environments.
The company said the capabilities integrate the Denodo Platform with AWS services, including Amazon Bedrock AgentCore, Amazon SageMaker and Amazon Quick, enabling organisations to provide AI agents with governed, real-time access to enterprise data spread across on-premise systems, software-as-a service platforms and cloud environments.
The move comes as enterprises in sectors such as banking, healthcare, manufacturing, retail and government increasingly seek to operationalise AI rather than limit deployments to proofs of concept.
“Agentic AI requires more than powerful models,” said Suresh Chandrasekaran, executive vice-president at Denodo. “It requires trusted, real-time and well-governed data.
“Our collaboration with AWS focuses on delivering a unified data foundation that enables organisations to scale AI agents with confidence across the entire data landscape.”
Addressing the data challenge behind enterprise AI
Many AI initiatives fail to progress beyond pilot stages because agents often operate with incomplete datasets, outdated information or limited business context. This challenge is becoming increasingly important in the Middle East, where governments and enterprises are pursuing ambitious national AI agendas while navigating strict regulatory and sovereignty requirements.
Countries including the UAE and Saudi Arabia have made AI a central pillar of digital transformation strategies, with initiatives spanning smart cities, digital government, financial services modernisation and industrial automation.
However, enterprises in the region frequently manage highly distributed environments that combine legacy infrastructure, sovereign cloud deployments, regional datacentres and multiple cloud providers. This creates significant complexity when AI agents need secure access to operational and analytical data.
For Middle East organisations operating under increasingly stringent data governance frameworks, particularly in financial services and public sector environments, this model could help balance AI innovation with regulatory compliance.
The announcement is likely to resonate in Gulf markets where sovereign AI strategies are gaining momentum. Regional governments are investing heavily in national AI capabilities, sovereign cloud infrastructure and trusted data ecosystems to support sectors ranging from healthcare and energy to public administration.
Financial institutions in the GCC, for example, are exploring AI-powered customer engagement, fraud detection and autonomous operations, while healthcare providers are evaluating AI agents for clinical workflows and operational efficiency.
Yet these use cases require strong governance, lineage tracking and role-based access controls, areas where data platforms increasingly play a critical role.
Denodo said its integration with Amazon SageMaker Catalog adds semantic context, business metadata and governance information directly into AI workflows, helping agents interpret enterprise data more accurately.
The platform also supports fine-grained governance controls, including attribute-based access policies, dynamic data masking and end-to-end lineage visibility across non-AWS environments. This is particularly relevant for Middle East organisations adopting multi-cloud strategies while retaining sensitive workloads in sovereign infrastructures.
From analytics to autonomous workflows
Beyond data access and governance, Denodo is also targeting business users seeking faster AI-driven decision-making. Its integration with Amazon Quick allows organisations to combine AI workflows with real-time enterprise data without replicating information between systems.
The company said this could accelerate development of conversational experiences, automated processes and autonomous workflows while reducing time-to-market.
For enterprises across the Middle East, where digital transformation programmes increasingly focus on measurable outcomes rather than experimentation, the ability to move from insight to action may prove critical.
As regional organisations continue to expand AI investments, the industry’s focus appears to be shifting away from model performance alone and towards the underlying data architecture needed to support autonomous systems at scale.
