RPA vs Agentic AI: Key Differences Explained

Learn the differences between RPA and Agentic AI, with examples of use cases and how they can work together for maximum efficiency.

Introduction

In the era of digital transformation, organizations are looking for technologies to increase efficiency and reduce operational costs. Two technologies that have gained significant attention are RPA (Robotic Process Automation) and Agentic AI. While both aim to automate work processes, they have important differences in concepts and operations.

What is RPA?

RPA (Robotic Process Automation) is technology that uses software robots or "bots" to simulate and replicate human actions in interacting with digital systems. RPA works by recording and repeating predefined work steps.

Key Characteristics of RPA:

• Rule-based: Works according to clearly defined rules and steps
• Structured Data: Works well with clearly structured data
• Repetitive Tasks: Suitable for repetitive tasks with fixed patterns
• No Learning: Cannot learn or adapt from experience

What is Agentic AI?

Agentic AI is an artificial intelligence system capable of making decisions and acting autonomously to achieve set goals. This system can analyze situations, plan, and adjust working methods according to context.

Key Characteristics of Agentic AI:

• Goal-oriented: Works to achieve goals rather than following steps
• Adaptive: Can adapt and learn from experience
• Context-aware: Understands context and can make decisions based on situations
• Complex Problem Solving: Handles complex problems without fixed patterns

Comparison Table

Comparison Dimension	RPA	Agentic AI
Operation	Follows predefined steps (Rule-based)	Makes decisions and plans autonomously (Goal-based)
Flexibility	Low - Can only do what's programmed	High - Adapts to situations
Learning	No learning capability	Can learn and self-improve
Task Types	Repetitive tasks with clear patterns	Complex tasks requiring decision-making
Error Handling	Stops when encountering unexpected situations	Can resolve and find new alternatives
Development Cost	Low-Medium	High
Usage Complexity	Easy - Can use Low-code/No-code	Complex - Requires AI expertise

Use Case Examples

RPA is suitable for:

1. Data Entry: Copying data from Excel files to ERP systems
2. Report Generation: Pulling data from multiple systems to create monthly reports
3. Invoice Processing: Reading invoice data and recording it in accounting systems
4. Data Verification: Comparing data between different systems

Agentic AI is suitable for:

1. Customer Service: Chatbots that understand context and solve complex problems
2. Investment Analysis: Analyzing markets and making automatic investment decisions
3. Production Planning: Adjusting production plans based on demand and supply chain
4. Medical Diagnosis: Analyzing symptoms and recommending treatments

Working Together: RPA + Agentic AI

In practice, organizations can use both RPA and Agentic AI together for maximum efficiency:

1. Agentic AI as the brain: Analyzes, decides, and plans work
2. RPA as the hands: Executes commands and steps defined by AI

Examples of Combined Usage:

• Order Management System: Agentic AI analyzes orders and decides processes while RPA handles data entry and email sending
• Document Verification: Agentic AI analyzes and classifies documents, RPA handles storage and forwarding as specified

Considerations for Selection

Choose RPA when:

• Tasks have clear steps that don't change frequently
• Quick ROI is needed
• Budget is limited
• Team lacks AI expertise

Choose Agentic AI when:

• Tasks require complex decision-making
• Systems need to adapt to situations
• Large amounts of data are available for learning
• Ready for long-term investment

Conclusion

RPA and Agentic AI each have their own strengths and limitations. RPA is suitable for automation with clear patterns, while Agentic AI is suitable for tasks requiring decision-making and adaptation. Technology selection should consider task characteristics, budget, and organizational goals.

In the future, we're likely to see more integration of both technologies, with Agentic AI acting as the "brain" that thinks and decides, while RPA acts as the "hands" that execute commands. This collaboration will help organizations fully leverage automation benefits.