A recent study by researchers Ranjan Sapkota, Konstantinos I. Roumeliotis, and Manoj Karkee delves into the nuanced differences between AI Agents and Agentic AI, providing a structured taxonomy, application mapping, and an analysis of the challenges inherent to each paradigm.
Defining AI Agents and Agentic AI
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AI Agents: These are modular systems primarily driven by Large Language Models (LLMs) and Large Image Models (LIMs), designed for narrow, task-specific automation. They often rely on prompt engineering and tool integration to perform specific functions.
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Agentic AI: Representing a paradigmatic shift, Agentic AI systems are characterized by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy. They move beyond isolated tasks to coordinated systems capable of complex decision-making processes.
Architectural Evolution
The transition from AI Agents to Agentic AI involves significant architectural enhancements:
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AI Agents: Utilize core reasoning components like LLMs, augmented with tools to enhance functionality.
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Agentic AI: Incorporate advanced architectural components that allow for higher levels of autonomy and coordination among multiple agents, enabling more sophisticated and context-aware operations.
Applications
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AI Agents: Commonly applied in areas such as customer support, scheduling, and data summarization, where tasks are well-defined and require specific responses.
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Agentic AI: Find applications in more complex domains like research automation, robotic coordination, and medical decision support, where tasks are dynamic and require adaptive, collaborative problem-solving.
Challenges and Proposed Solutions
Both paradigms face unique challenges:
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AI Agents: Issues like hallucination and brittleness, where the system may produce inaccurate or nonsensical outputs.
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Agentic AI: Challenges include emergent behavior and coordination failures among agents.
To address these, the study suggests solutions such as ReAct loops, Retrieval-Augmented Generation (RAG), orchestration layers, and causal modeling to enhance system robustness and explainability.
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