Generative AI, AI Agents, and Agentic AI : An Overview of Current AI Technologies

Abstract

This research paper examines the transformation of Artificial Intelligence from rule-based systems to three advanced paradigms: Generative AI, AI Agents, and Agentic AI. Generative AI focuses on creating original content by learning from large datasets, serving as reactive tools that respond to user prompts. AI Agents execute goal-driven tasks autonomously within specific environments by perceiving inputs and making decisions in real time. Agentic AI advances this further by proactively planning, reasoning, and managing multi-step workflows with minimal human oversight, enabling dynamic task execution and long-term goal achievement. The study highlights how these AI forms differ in autonomy, function, and application, emphasizing the growing impact of Agentic AI in automating complex processes. Understanding these distinctions is essential for designing responsible AI systems that balance innovation, ethics, and human collaboration in an evolving technological landscape.

Introduction

Artificial Intelligence (AI) has advanced from simple rule-based systems to highly autonomous and creative technologies, now broadly categorized into Generative AI, AI Agents, and Agentic AI. Understanding these distinctions is essential as AI becomes increasingly embedded in daily life.

1. Generative AI

• Purpose: Produces original content (text, images, music, code) by learning patterns from large datasets.

• Mechanism: Uses models like transformers and GANs to predict outputs based on learned patterns.

• Applications: Content creation (articles, music, art), design, healthcare (synthetic images), and education (personalized learning).

• Advantages: Stimulates creativity, reduces time/cost, personalizes outputs, spurs innovation.

• Challenges: Bias, copyright issues, misinformation, privacy concerns.

• Examples: ChatGPT, DALL•E.

2. AI Agents

• Purpose: Autonomously perceive, decide, and act within specific tasks.

• Mechanism: Operate via a Perception–Decision–Action loop, enabling adaptation in dynamic environments.

• Categories:

  1. Simple Reflex Agents – respond immediately to stimuli.

  2. Model-Based Agents – maintain internal environment models.

  3. Goal-Based Agents – act to achieve objectives.

  4. Learning Agents – improve from experience.

• Applications: Virtual assistants (Siri, Alexa), customer service bots, industrial or household robots, self-driving cars, game bots.

• Focus: Task-oriented autonomy, executing predefined or learned actions.

3. Agentic AI

• Purpose: Combines generative creativity and agentic autonomy to plan, reason, and execute complex multi-step tasks independently.

• Capabilities: Proactively manages workflows, maintains long-term goals, interacts with external systems, adapts strategies based on outcomes.

• Applications: Automated marketing campaigns, business process automation, multi-agent collaborations.

• Future Trends:

  • Self-learning and continuous improvement.

  • Collaboration among multiple AI systems.

  • Integration of ethical frameworks and governance.

• Examples: AutoGPT, BabyAGI.

• Impact: Transforms AI from reactive tools into autonomous digital collaborators, reshaping workforces and creating new roles in oversight, ethics, and AI management.

Comparison Table

Key Takeaway:

• Generative AI reacts to prompts and creates content.

• AI Agents autonomously perform specific tasks.

• Agentic AI proactively orchestrates multi-step processes to achieve long-term goals, representing the next stage of autonomous, collaborative AI.

Conclusion

The evolution from generative AI fostering creativity, to AI agents managing automation, and now to Agentic AI enabling autonomous, multi-step reasoning marks a transformative era of intelligent cooperation between humans and machines. This paradigm shift promises to enhance innovation, boost operational efficiency, and bring significant changes across societal and industrial domains. To fully harness these benefits, it is crucial to align technological advancement with ethical principles, ensuring responsible development and deployment of AI systems. Emphasizing transparency, accountability, and human oversight will help guarantee that AI serves as a beneficial partner rather than an unchecked force, paving the way for a future where human ingenuity and AI capabilities collaboratively drive progress and positive impact.

References

[1] K. Roberts et al., \"Agentic AI for Autonomous Workflow Management,\" International Journal of AI Research, vol. 48, no. 3, pp. 256–268, 2025. [2] J. Smith and K. Patel, \"Autonomous AI Agents in Robotics,\" Journal of Artificial Intelligence Research, vol. 68, pp. 123–145, 2023. [3] M. Lee et al., \"Agentic AI: Multi-agent Collaboration and Autonomous Reasoning,\" Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 9, pp. 10012–10020, 2024. [4] S. Gupta and A. Kumar, \"Ethics and Governance in AI Systems,\" IEEE Access, vol. 10, pp. 23000–23012, 2022. [5] R. Davis and M. Thompson, \"Human-AI Collaboration: Enhancing Productivity and Accountability,\" International Journal of Human-Computer Studies, vol. 159, pp. 102681, 2022. [6] C. Lin and D. Wang, \"Perception, Decision, and Action Loop in AI Agents,\" IEEE Transactions on Systems, Man, and Cybernetics, vol. 51, no. 11, pp. 6868–6878, Nov. 2021.

Copyright

Copyright © 2025 Dr. Urmila R. Pol. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.