From Generative AI to Agentic AI

Understanding the Coming Shift: From Generative AI to Agentic AI

Large Language Models (LLMs), such as GPT, excel at generating text, answering questions, and supporting various tasks. However, they operate reactively, responding only to the input they receive based on learned patterns. LLMs cannot make decisions independently, adapt to new situations, or plan ahead.

Agentic AI addresses these limitations. Unlike Generative AI, Agentic AI can set goals for itself, take initiative by itself, and learn from its experiences. It is proactive, capable of adjusting its actions over time, and can manage complex, evolving tasks that demand continuous problem-solving and decision-making. This transition from reactive to proactive AI unlocks exciting new possibilities across industries.

In this insight, we will explore the differences between Agentic AI and Generative AI, examining their distinct impacts on technology and industries. Let’s begin by understanding what sets them apart.

What is Agentic AI?

Agentic AI refers to systems capable of autonomous decision-making and action to achieve specific goals. These systems go beyond generating content—they interact with their environments, respond to changes, and complete tasks with minimal human guidance.

For example:

A virtual assistant with agentic capabilities could schedule appointments, manage reminders, or complete tasks to help users achieve their objectives.
Self-driving and connected cars exemplify Agentic AI, making real-time decisions to safely navigate roads and reach destinations independently.

What is Generative AI?

Generative AI focuses on creating content—text, images, music, or video—by learning from large datasets to identify patterns, styles, or structures.

For instance:

Models like ChatGPT generate unique text responses to prompts.
Image generators like DALL-E create visuals from text descriptions.

Generative AI acts like a creative assistant, producing content based on what it has learned, but it remains reactive and task-specific.

Key Differences in Workflows

Agentic AI employs an iterative, cyclical workflow that includes stages like “Thinking/Research” and “Revision.” This adaptive process involves self-assessment, testing, and refinement, enabling the system to learn from each phase and tackle complex, evolving tasks effectively.

Generative AI, in contrast, follows a linear, single-step workflow, moving directly from input to output without iterative improvements. While efficient for straightforward tasks, it lacks the ability to revisit or refine its results, limiting its effectiveness for dynamic or nuanced challenges.

Characteristics of Agentic AI vs. Generative AI

Feature	Agentic AI	Generative AI
Autonomy	Acts independently, making decisions and executing tasks.	Requires human input to generate responses.
Behavior	Goal-directed, proactively working toward specific objectives.	Task-oriented, reacting to immediate prompts.
Adaptation and Learning	Learns from experiences, adjusting actions dynamically.	Operates based on pre-trained patterns, without learning.
Decision-Making	Handles complex decisions, weighing multiple outcomes.	Makes basic decisions, selecting outputs based on patterns.
Environmental Perception	Understands and interacts with its surroundings.	Lacks awareness of the physical environment.

Case Study: Agentic Workflow in Action

Andrew Ng highlighted the power of the Agentic Workflow in a coding task. Using the HumanEval benchmark, his team tested two approaches:

Zero-shot prompting: AI was asked to solve the problem directly. GPT-3.5 achieved 48% accuracy, and GPT-4 scored 67%.
Agentic Workflow: The task was broken into steps (understanding, coding, testing, and debugging). With this approach, GPT-3.5 outperformed GPT-4 using zero-shot prompting, and GPT-4 performed even better when using the Agentic Workflow.

This illustrates how iterative methods can enhance performance, even for older AI models.

Conclusion

As AI becomes increasingly integrated into our lives and workplaces, understanding the distinction between Generative AI and Agentic AI is essential. Generative AI has transformed tasks like content creation, offering immediate, reactive solutions. However, it remains limited to following instructions without true autonomy.

Agentic AI represents a significant leap in technology. By setting goals, making decisions, and adapting in real-time, it can tackle complex, dynamic tasks without constant human oversight. Approaches like the Agentic Workflow further enhance AI’s capabilities, enabling iterative learning and continuous improvement.