Agentic AI Offers a Leap Forward in Digital Transformation
Mohammed AlMoneer, the Sr. Regional Director for Middle East, Africa, and Turkey at Infoblox, says autonomy should be calibrated based on risk, context, and regulatory requirements
How do you define Agentic AI, and how does it fundamentally differ from traditional chatbots or rule-based automation?
At Infoblox, we define Agentic AI as a class of intelligent systems capable of autonomous, goal-driven behavior across complex environments. Unlike traditional chatbots or rule-based automation – which operate within predefined scripts or static workflows – Agentic AI dynamically interprets context, adapts to new data, and makes decisions to achieve outcomes without constant human oversight.
In the Middle East, where digital transformation is accelerating across government and enterprise sectors, Agentic AI offers a leap forward. For example, in our work with other regional governments, we’ve explored how Agentic AI can automatically secure national domain spaces – identifying malicious lookalike domains and initiating takedown actions without manual intervention.
Beyond task automation, what complex, multi-step problems are Agentic AIs uniquely positioned to solve that current AI models cannot?
Agentic AI excels in orchestrating multi-step, cross-domain workflows that require contextual awareness, decision-making, and adaptation. At Infoblox, we see this in cybersecurity use cases where agents can detect anomalies in DNS traffic, correlate them with threat intelligence, and autonomously trigger mitigation actions or alarm the human defenders where needed. Agentic AI helps deliver a seamless proactive defense posture. It’s not just about reacting to alerts – it’s about anticipating threats and acting preemptively, which is critical in sectors like finance, energy, and government.
How much autonomy should Agentic AI have in decision-making, and where should humans remain in the loop?
Autonomy should be calibrated based on risk, context, and regulatory requirements. We advocate for a “human-in-the-loop” model – where AI agents operate independently within defined boundaries but escalate decisions when thresholds are exceeded or ambiguity arises.
In the Middle East, where regulatory frameworks are evolving, this balance is especially important. For example, in government deployments, Agentic AI can automate domain takedowns as mentioned but still require human validation for more sensitive actions.
What infrastructure challenges (such as power, cooling, latency) arise when deploying Agentic AI at scale?
Deploying Agentic AI at scale introduces several infrastructure challenges. These include:
- Security and Compliance: Ensuring secure data flows and auditability across distributed agents is non-trivial.
- Latency: Agents require real-time data access and decision execution, which demands low-latency networks – especially critical in edge environments like smart cities or oil & gas fields.
- Integration Challenges: Integrating agentic AI systems with existing legacy systems and diverse software applications can be challenging due to differing architectures and protocols.
- Compute and Cooling: Running large models or coordinating multiple agents can strain data center resources, particularly in hot climates like the Gulf region.
To meet these challenges, companies should invest in infrastructure that’s secure, cloud-native, and built for scale.
What’s the next frontier for Agentic AI – will we see AI “agents” collaborating like human teams?
Absolutely. We’re already seeing early signs of “agent swarms” where multiple AI agents collaborate – each with specialized roles. At Infoblox, we envision agents that handle threat detection, policy enforcement, and remediation working in tandem, making the work of defenders easier and enabling them to focus on the topics and alerts that really matter. In the UAE, where national cybersecurity strategies emphasize automation and resilience, this model aligns well with the vision of autonomous digital defense ecosystems.
Will open-source models keep pace with proprietary Agentic AI systems, or will there be a widening gap?
Open-source models are advancing rapidly and play a vital role in democratizing AI. However, proprietary systems often lead in areas like security, compliance, and integration – especially in regulated sectors. At Infoblox, we are adopting a hybrid approach: leveraging open-source innovation while building proprietary capabilities that meet the stringent demands of our customers in the Middle East and beyond. This ensures flexibility without compromising on trust or performance.
