Integrating Agentic AI into Existing Infrastructures Without Causing Disruption is Complex
Sid Bhatia, the Area VP & General Manager – Middle East, Turkey & Africa at Dataiku, says, feedback loops let agents learn from human corrections, reducing future intervention needs
How do you define Agentic AI, and how does it fundamentally differ from traditional chatbots or rule-based automation?
Agentic AI refers to artificial intelligence systems designed to operate with a degree of autonomy, enabling them to make decisions and solve problems independently. Unlike traditional AI, which works within predefined rules and parameters, or generative AI, which focuses on creating outputs like text, images, or code, agentic AI is goal-oriented and capable of adaptive decision-making. It combines real-time feedback, long-term planning, and contextual understanding, making it more versatile in complex, dynamic environments.
It achieves autonomy by leveraging reinforcement learning, dynamic contextual understanding, and self-optimization. Reinforcement learning allows it to refine decisions based on rewards and penalties, while contextual understanding enables it to interpret and respond to evolving situations in real time. Self-optimization ensures that the system continuously updates its internal models and strategies to improve performance. However, the boundaries of its “agency” are defined by its design, including ethical guidelines, regulatory requirements, and operational constraints. While it operates autonomously, it remains limited by the parameters set during its development and deployment.
How much autonomy should Agentic AI have in decision-making, and where should humans remain in the loop?
Striking the right balance requires tiered autonomy: Low-risk decisions (e.g., routine data processing) can, and should, be fully automated, while high-impact actions (e.g., financial approvals) should trigger human review. Asynchronous oversight, where humans audit decisions post-execution rather than pre-approving them, will help preserve speed without sacrificing accountability.
Also feedback loops let agents learn from human corrections, reducing future intervention needs. By defining clear thresholds and leveraging tools like confidence-score-based escalations, enterprises maintain control without undermining efficiency.
What infrastructure challenges (such as power, cooling, latency) arise when deploying Agentic AI at scale?
Generalization is a significant hurdle, as the AI must be capable of transferring its learning across diverse tasks and domains. Explainability is another critical challenge; systems must articulate the reasoning behind their decisions to ensure trust and transparency. High-quality, unbiased, and comprehensive training data are essential for the AI to function effectively. Also, real-time adaptation requires algorithms capable of processing and reacting to vast amounts of data instantaneously. Integrating agentic AI into existing infrastructures without causing disruption is also complex, and robust safety mechanisms must be developed to prevent unintended consequences.
