AI Agents vs. Agentic AI: Understanding the Next Frontier in Artificial Intelligence

Introduction: Why Clarifying These Concepts Matters

The rapid rise of artificial intelligence has brought with it new challenges in how we define and classify intelligent systems. One of the most important yet misunderstood distinctions today is between AI agents and agentic AI. The 2025 paper AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges by Ranjan Sapkota, Konstantinos I. Roumeliotis, and Manoj Karkee introduces a clear taxonomy to separate these two concepts, helping researchers, developers, and policymakers navigate the complex landscape of AI autonomy and intelligence.

This article explores the core insights from that paper, including definitions, differences, real-world applications, and future research directions.

What Is the Difference Between AI Agents and Agentic AI?

Defining AI Agents

An AI agent is a "Autonomous software programs that perform specific tasks" Sapkota et al. (2025). These systems operate within structured environments and are often built to solve specific tasks.

Typical AI agents:

• Follow predefined rules or trained policies

• React to user inputs or environmental changes

• Have limited or task-specific capabilities

Examples: chatbots, recommendation engines, robotic arms in factories

Defining Agentic AI

Agentic AI, on the other hand, represents "Systems of multiple AI agents collaborating to achieve complex goals." Sapkota et al. (2025). It refers to systems that exhibit agency—the ability to pursue goals, reason about decisions, adapt to changing environments, and act proactively.

Agentic AI systems:

• Form and pursue their own goals

• Operate beyond narrow task constraints

• Can explain or justify their actions

• Possess internal models of the world and themselves

According to the paper, agentic AI aligns more closely with general intelligence and open-ended problem-solving.

Context and Relevance of the Study

As LLMs (Large Language Models), autonomous robots, and planning systems evolve, researchers have used the term "agent" more frequently—often ambiguously. The lack of clear definitions has led to confusion in AI research, regulation, and deployment.

The study argues that distinguishing between AI agents and agentic AI is not a semantic debate, but a foundational need for:

• Designing better evaluation frameworks

• Aligning AI behavior with human goals

• Managing safety and accountability in autonomous systems

Main Findings and Contributions

The research presents a novel contribution to AI discourse by:

• Clarifying terminology around agents vs. agency

• Structuring a framework for categorizing intelligent systems

• Identifying gaps in current approaches to AI development and evaluation

• Highlighting the potential risks and responsibilities that come with agentic AI

Practical Applications in Industry

Where AI Agents Are Used Today

AI agents are already integrated into various sectors:

• Agriculture: systems that monitor irrigation or detect pests

• Manufacturing: automated inspection and quality control

• Finance: rule-based trading algorithms

• Customer Service: chatbots and workflow automation

These systems perform well in predictable, structured environments with limited scope.

Where Agentic AI Could Transform the Future

Agentic AI could revolutionize:

• Healthcare: adaptive diagnostic assistants capable of longitudinal patient tracking

• Autonomous Vehicles: decision-making systems that learn and adapt in real time

• Scientific Research: AI that proposes and tests hypotheses

• Education: personalized tutors that adapt to individual learning paths

These systems require more than reactivity—they need planning, learning, and the ability to justify their decisions.

Key Takeaways and Future Directions

• Not all AI agents are agentic: Systems can be interactive and intelligent without possessing full decision-making autonomy.

• Agentic AI requires new safeguards: As systems grow more autonomous, developers must ensure alignment with human goals.

• Taxonomies help structure the field: Clarifying concepts aids regulation, evaluation, and public understanding.

Final Reflection

The paper AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges (arXiv:2505.10468) by Sapkota, Roumeliotis, and Karkee (2025) makes a significant contribution by untangling two fundamental ideas in AI development. It provides a structured way to understand how intelligent systems think, act, and evolve—and why that matters for safety, ethics, and innovation.

This work invites researchers, developers, and policymakers to move beyond the “agent” label and start asking deeper questions about autonomy, accountability, and intelligent behavior in machines.

Read the full paper here: https://arxiv.org/abs/2505.10468

#ArtificialIntelligence #AgenticAI #AIAgents #AIResearch #MachineLearning #AIAlignment #AutonomousSystems #LLMReady #AIPlanning #IAExplicada #AIExplained