Combining GenAI & Agentic AI to build scalable, autonomous systems

Understanding Agentic AI GenAI is great at creation, while Agentic AI brings decision-making and execution Strategic considerations for tech leaders Posted on August 18, 2025 by Marta Fernandes, Engineering Operations Manager, YLD A common pattern in today’s AI adoption is that businesses are investing heavily in GenAI capabilities, yet many are leaving significant value on the table by failing to pair it with Agentic AI. This matters because while GenAI can generate content, ideas, or responses, it can’t act on them. However, when combined with Agentic AI, your systems shift from being reactive and prompt-driven to autonomous and outcome-oriented. This article aims to help senior technology leaders better understand the value Agentic AI can add to existing AI infrastructure, while outlining key considerations to determine whether its implementation aligns with your business needs. Image caption: Core components of an Agentic AI Architecture from Markovate. Agentic AI systems are designed to operate autonomously, perceiving their environment, making decisions, and executing actions without continuous human oversight. This autonomy enables businesses to streamline operations, reduce costs, and enhance scalability. The image above illustrates how the following systems function: Perception layer: This layer involves collecting data from various sources i. e. sensors, cameras, microphones, and digital inputs, to understand the current state of the environment. For businesses, this means real-time monitoring of operations, customer interactions, and market trends, facilitating proactive decision-making and rapid response to changing conditions. Cognition layer: Once data is collected, the cognition layer processes this information to interpret context, recognise patterns, and determine the best course of action.