1. Introduction

Nvidia, Microsoft, and OpenAI: Everyone is suddenly talking about Agentic AI. So what’s driving this new evolution?

Two main forces are fueling the rise of Agentic AI. First, the pressure to monetize large language models (LLMs) like ChatGPT and Gemini. Second, the ‘Law of Scaling’ that limits how far incremental improvements can take traditional LLMs further, even when adding new features.

But what exactly is Agentic AI? How does it differ from Generative AI? And how do they complement each other? Most importantly, how will Agentic AI shake and transform the world of Robotic Process Automation (RPA)?

As businesses look to the future, understanding the distinction between Generative AI and Agentic AI becomes crucial. Generative AI is about creating content—text, images, music—while Agentic AI is about taking autonomous actions to achieve specific goals. Imagine pairing the content creation power of Generative AI with the autonomy of Agentic AI—the possibilities for automation and innovation are limitless.

What is Agentic AI?

Agentic AI refers to intelligent systems that possess agency—the ability to act independently with purpose. These systems excel in:

1. Reasoning: Analyzing complex information to draw logical conclusions.
2. Planning: Creating strategies to achieve goals while navigating uncertainties.
3. Learning: Adapting to new data and improving performance over time.
4. Action: Performing tasks autonomously in digital or physical environments.

Key Differences: Traditional AI vs. Agentic AI

Feature	Traditional AI	Agentic AI
Initiative	Relies on human input and guidance	Proactively identifies opportunities and acts
Autonomy	Limited by predefined rules	Capable of independent decision-making
Learning	Requires human intervention for updates	Continuously learns and adapts autonomously

How Agentic AI Works

Agentic AI operates in a cyclical process to deliver its unique capabilities:

1. Perception: Collecting and interpreting data from multiple sources.
2. Reasoning: Using advanced models to analyze information and generate solutions.
3. Action: Executing tasks via APIs or directly interacting with environments.
4. Learning: Refining its abilities through feedback and data analysis.
   

2 Agentic AI vs. Generative AI: Partners in Innovation

Generative AI and Agentic AI represent two distinct yet complementary branches of artificial intelligence.

Feature	Generative AI	Agentic AI
Primary Function	Content Creation	Action and Decision-Making
Focus	Generating new outputs	Executing tasks autonomously
Human Interaction	Requires input for creative guidance	Operates independently with minimal input

How They Complement Each Other:

• Creativity Meets Efficiency: Generative AI creates ideas; Agentic AI implements them.
• Enhanced Decision-Making: Generative AI provides insights; Agentic AI acts on them.
• Superior Customer Experience: Generative AI personalizes content; Agentic AI delivers it effectively.
  

3. Agentic AI vs. RPA: A New Era of Automation

While Robotic Process Automation (RPA) has streamlined repetitive tasks, Agentic AI offers a more advanced, adaptable alternative.

Feature	RPA	Agentic AI
Scope	Rule-based tasks	Complex, dynamic processes
Adaptability	Requires reprogramming for changes	Learns and adapts autonomously
Decision-Making	Follows predefined rules	Makes data-driven decisions
Complexity Handling	Struggles with unstructured data	Excels in complexity, including unstructured data

4. Practical Use Case of Agentic AI in Banking: Proactive Fraud Detection and Prevention

Scenario: A bank deploys an Agentic AI system to enhance fraud detection and prevention in real time. Here’s how it works:

1. Perception:
   The Agentic AI system continuously monitors transactions across millions of accounts, analyzing data from diverse sources like customer behavior patterns, transaction histories, and external threat intelligence feeds.
2. Reasoning:
   When the system detects anomalies—such as an unusually high withdrawal from an account in a different country—it assesses the context. Using advanced models, it compares the current transaction with the customer’s typical patterns, evaluates the risk level, and determines if fraud is likely.
3. Action:
   If the system concludes that the transaction is suspicious, it takes immediate steps:
   • Temporarily freezes the transaction.
   • Sends an alert to the customer via their preferred communication channel (app notification, SMS, or email) to confirm or dispute the transaction.
   • Notifies the bank’s fraud team with a detailed risk assessment report.
4. Learning:
   Over time, the Agentic AI refines its fraud detection models by learning from customer feedback (e.g., flagged transactions deemed legitimate) and new data patterns, becoming better at distinguishing between real threats and false positives.

Outcome:

• Enhanced Security: The system proactively blocks fraudulent transactions before they are processed, reducing financial losses and protecting customer accounts.
• Improved Customer Experience: The swift and automated response ensures minimal disruption to legitimate banking activities while maintaining trust.
• Operational Efficiency: By automating fraud detection and initial responses, the bank reduces the workload on its fraud management team, allowing them to focus on complex cases.

Why it Matters: Traditional fraud detection systems rely heavily on predefined rules and human intervention, which can lead to delays or missed fraud attempts. In contrast, Agentic AI operates autonomously, adapting to evolving fraud techniques in real time, providing banks with a cutting-edge advantage in securing transactions.

This example showcases how Agentic AI revolutionizes banking by combining real-time decision-making with proactive actions, ensuring a safer and more efficient financial ecosystem.

5. Conclusion

Agentic AI is more than just an innovation—it’s a paradigm shift. By combining the creative prowess of Generative AI with the autonomy of Agentic AI, industries can unlock unprecedented efficiencies and address complex challenges.

Its poised to elevate Robotic Process Automation from simple rule-based automation to a new level of intelligent, adaptive automation. The impact will ripple across industries, transforming how processes are optimized, decisions are made, and value is delivered.

References:

https://blogs.nvidia.com/blog/what-is-agentic-ai
https://www.microsoft.com/en-us/research/project/agent-ai/
https://github.com/openai/swarm