Why clear definitions of agentic AI matter now more than ever

Agentic AI represents a significant architectural approach that combines multiple technologies to enable autonomous, goal-oriented systems that can plan and execute complex tasks with minimal human intervention. As organizations rush to incorporate these capabilities, the lack of standardized definitions has led to confusion, exaggerated claims, and “agentic washing,” where solutions are falsely marketed as agentic AI. This definitional chaos makes it impossible to identify appropriate use cases, metrics, and return on investment for these emerging technologies.

The big picture: Agentic AI is not a single application or technology but an architecture that integrates various components to create highly autonomous, goal-oriented AI agents capable of independent decision-making and task execution.

• These agents leverage foundation models, advanced machine learning techniques, memory modules, and external tools to plan and execute complex tasks with minimal human intervention.
• A particularly significant development is multi-agent systems, where specialized AI agents work within a coordinated framework to achieve common objectives.

Key capabilities: Agentic AI systems are characterized by several distinctive technological approaches that enable their autonomy and effectiveness.

• Chain-of-thought reasoning allows these systems to work through problems methodically.
• Reinforcement learning techniques help the systems improve over time.
• Multi-modal capabilities enable these systems to process and respond to different types of data inputs.

Technology relationships: Agentic AI has important distinctions from other automation technologies while potentially working alongside them in enterprise ecosystems.

• While generative AI focuses primarily on content creation, agentic AI is distinguished by its autonomous action capabilities, though GenAI can serve as a component within agentic systems.
• Robotic Process Automation (RPA) handles deterministic automation of structured tasks, while agentic AI operates probabilistically with unstructured data, offering complementary capabilities.

Strategic implications: The emergence of agentic AI provides organizations with potentially transformative business capabilities beyond current automation approaches.

• These systems offer potential improvements in productivity, efficiency, and cost savings across various business functions.
• Agentic AI represents a natural extension of generative AI capabilities, adding autonomous action to the content generation abilities of current systems.
• The technology enables intelligent, end-to-end workflows that can operate with minimal human supervision.

Why this matters: The confusion around agentic AI definitions has serious consequences for technology adoption and investment decisions.

• Unrealistic expectations about what agentic AI can achieve make it impossible to identify appropriate use cases and metrics for success.
• Without clear definitions, organizations cannot accurately assess the value and return on investment of proposed agentic AI solutions.
• Omdia has developed a comprehensive framework for understanding and assessing agentic AI in a two-part report series released in May 2025.