Beyond Buzzwords: Why Rigorous Definitions Matter in the Agentic AI Conversation

Agentic AI offers transformative potential through autonomous decision-making and task execution, its widespread hype has led to 'agentic washing' and unrealistic expectations. Industry expert Eden Zoller shares Omdia's robust framework for defining, understanding, and assessing its true value. 

This blog provides you with Omdia's comprehensive framework for understanding agentic AI, clarifying its defining attributes, distinguishing it from generative AI and RPA, and offering insights into its strategic business value amid growing hype and misconceptions.

The Promise and Peril of Agentic AI

Agentic AI agents are capable of elevated levels of autonomy decision-making and task execution with the potential for transformative results. This has sparked legitimate excitement, but also massive hype is causing confusion and fueling exaggerated claims. This is tipping into ‘agentic washing’ where services and solutions are presented as agentic AI when they are not. Unrealistic expectations as to what agentic AI can achieve and deliver makes it impossible to identify appropriate use cases and metrics for agentic AI solutions, which in turn makes it impossible to ascertain the value of agentic AI and return on investment. This is why a robust, shared definition for agentic AI is so important, and where Omdia has created a compelling framework for understanding and assessing agentic AI, developed across a two-part report series*.  

Agentic AI is not an application or single technology but rather an architecture that brings together a range of technologies, techniques and components that in combination enable the creation of goal-oriented AI agents that are highly autonomous. Core, defining agentic AI attributes are highlighted in Figure 1, and understanding them is the first step in grounding agentic AI. AI agents use foundation models and advanced Machine Learning (ML) techniques, memory modules and external tools to proactively plan and execute complex tasks independently or with little human intervention. It is important to keep in mind that AI agent’s ability to reason, reflect, and learn is deeply influenced by the underlying capabilities model on which it is based—typically large language models (LLMs). Examples of important capabilities and associated ML techniques include Chain-of-thought (CoT) reasoning, Reinforcement Learning (RL), and multi-modal capabilities. A significant development in agentic AI is multi-agent systems where specialized agents operate within a coordinated framework to achieve common goals. Multi-agent systems enhance autonomy through parallel task execution and distributed workloads, offering greater adaptability and resilience.    

Source: Omdia

Building Effective AI Strategies with Agentic Capabilities

Omdia’s framework also clarifies the relationship between agentic AI and generative AI (GenAI), and traditional forms of robotic process automation (RPA). Understanding differences between these technologies clarifies how they can complement each other, and what is unique about agentic AI. This is crucial for designing effective AI strategies, from both a vendor roadmap and marketing perspective, and for enterprise deployment strategies. GenAI and agentic AI both leverage the power of foundation models (typically LLMs) and ML techniques and can be complementary, while GenAI can be a component of an agentic AI solution. The pairing leads to intelligent, end-to-end workflows, where generation supports action. The strategic investment drivers for agentic AI are like those for GenAI and are based on core business priorities, notably improved productivity, efficiency and cost savings. In this respect, agentic AI is a natural extension of GenAI capabilities, moving from generation to autonomous action. The strategic alignment between GenAI and agentic AI also has advantages for solution providers in terms of extending existing GenAI models, APIs, and data pipelines to support agentic capabilities. This in turn points to an expanded value proposition, where vendors can offer a full-stack AI solution from generation to autonomous action.  
However, GenAI and agentic AI differ in terms of autonomy, workflows and the nature of the output they produce. A GenAI agent focuses on content creation in response to a prompt, and pattern recognition from large datasets. Outputs are not usually produced by the underlying model independently redefining goals and proactively pursuing them, autonomously implementing multi-step planning or with the autonomous use of a potentially wide range of external tools that go beyond data retrieval mechanisms. 

The Evolution of Intelligent Automation: Where Agentic AI Fits

The nature and level of autonomy driven by agentic AI is a different order of magnitude compared to traditional RPA. RPA supports deterministic automation of structured, repetitive tasks, while agentic AI operates probabilistically to interpret unstructured data and adapt to unpredictable situations. These technologies can work synergistically within enterprise automation ecosystems, with Agentic AI providing decision-making capabilities for RPA or incorporating RPA bots as tools within its framework. However, RPA and agentic AI can be synergistic, especially in the wider context of an enterprise automation ecosystems, as opposed to an individual workflow. For example, an agentic AI layer can provide a decision layer for RPA to help it handle certain tasks better, such as context switching and managing exceptions. RPA bot could be a function in an agentic AI agent’s toolbox, or form part of a multi-agent agentic system where it would conduct simple automation tasks in the workflow. Viewed in this way, agentic can be seen as a continuation in the development of increasingly intelligent automation capabilities.   

Omdia’s deep expertise in AI provides the strategic insights you need to stay ahead. Connect with our analysts to learn how our research can support your AI strategy and help you navigate the evolving landscape.

*Understanding Agentic AI: Attributes, Architecture, and the Ecosystem   (May 2025)

*Maximizing Agentic AI: Capturing Benefits, Addressing Challenges  (May 2025)