Agentic AI vs RPA: differences, similarities, and examples

Enterprises are trying to automate everything today, and the technical landscape is being changed by Agentic AI and RPA (Robotic Process Automation). RPA works with exact, predefined instructions. While Agentic AI can aim to reach this goal, it plans and executes a sequence of tasks with minimal human intervention.

The potential of both approaches is huge. Many industries, including finance, insurance, healthcare, and customer service. These are already adopting Agentic AI in their internal systems to solve complex problems.

Both are trying to automate repetitive work and improve efficiency, but they achieve it in very different ways.

What is Agentic AI?

Agentic AI refers to an autonomous AI system. It can operate independently to achieve complex, long-term goals without constant human involvement. Agentic AI can understand its environment, reason, plan, act, and learn from its actions to become more efficient.

How it works

• Goal intake: The AI collects the information it needs to understand the objective and explain it in simple language
• Reasoning: The AI, powered by large language models (LLMs), analyzes data, plans all steps, and chooses the actions.
• Tool & data access: Connects with external APIs, software, and databases to collect information
• Orchestration: Breaks down complex tasks into smaller ones for easier execution. And planning their sequence to achieve the desired outcome
• Action: Execute the task order, working along internal and external systems, tools, and even other AI agents

Strengths

• Agents manage end-to-end workflows.
• Adjusts constantly when processes shift or data changes.
• Scales to support complex, multi-step operations.
• Removing humans from repetitive manual work.

Limitations

• Errors in one step can move through the process.
• Needs clear guardrails and human oversight to operate safely.
• Highly dependent on quality data.
• Requires transparency so organizations can track and understand decisions.

What is RPA (Robotic Process Automation)?

Robotic Process Automation (RPA) uses software bots to act like a human on a computer. It follows a predefined script-based path to complete tasks when working with digital systems. RPA systems allow enterprises to reduce errors and speed up operations while maintaining quality

How it works

• Process mapping: Every click, action, rule, and sequence itself is programmed in advance by IT teams.
• Execution: Bots replicate these steps across applications with speed, accuracy, and consistency.
• Scope: Ideal for large-scale, well-organized, and repetitive processes. For example, data entry, form filling, and transferring information between systems.
• Maintenance: Any change in process or interface requires updates to the scripts.

Strengths

• Reliable for high-volume, repetitive, structured tasks.
• Reduces human error and increases speed in routine business processes.
• Widely adopted in enterprise operations like finance, HR, and back-office functions.

Limitations

• Easily breaks when processes or systems change.
• Unable to process unstructured or dubious data.
• Dependent on IT teams for setup and maintenance.
• Limited scalability for complex or changing workflows.

What are the differences between Agentic AI vs RPA?

The two approaches reflect different philosophies of automation:

Agentic AI on adaptive goal achievement, while RPA focuses on the strict execution of predefined steps.

RPA and Agentic AI examples

Finance

• Agentic AI: Interprets invoices in varied formats, checks for differences. Updates the ERP, and can reach out to vendors if information is missing to collect the necessary data.
• RPA: Uses predefined templates to copy invoice data into an ERP system, following exact steps set up in advance.

Customer Support

• RPA: Routes tickets according to predefined rules, such as keywords or categories mapped ahead of time.
• Agentic AI: Reads entire customer messages and identifies intent. Then, respond to straightforward requests and pass along more complex cases for review.

Operations

• RPA: Transfers information between two systems on a predefined schedule. Based on the rules and execute scripted actions step by step.
• Agentic AI: Monitors system data constantly. Identifies discrepancies, updates records, and notifies stakeholders if unusual patterns appear.

What are the similarities between Agentic AI vs RPA?

Agentic AI and Robot Process Automation, despite their differences, share some core similarities:

• Both forms of automation technology aim to improve business effectiveness by reducing or removing human labor.
• Both aim to achieve 100% accuracy in operations.
• Both technologies try to integrate into the existing enterprise environments.
• Both aim to reduce business operational costs.
• Both are tools for business growth and more streamlined operations

What is Hyperautomation?

Hyperautomation is the moment robotic process automation, AI, machine learning, and analytics fuse into one. RPA, being the “muscle”, works along with agentic AI as a “brain” to streamline business processes and provide better customer interactions.

Hyperautomation is all about strategy and orchestration. Taking the right decision on what to automate with RPA and what to leave to the agentic AI. Chaining processes together for improved problem solving workflows.

What is Overautomation?

Automation itself is incredibly useful, but when you step into the fields of overautomation, it can become a burden.

Overautomation is when a business automates tasks and processes that actually do not need AI-driven workflows. Often, overautomation creates ineffectiveness and leads to a bad customer experience.

Overautomation can hurt customer experience by making customers feel detached and dealing with machines. As a consequence, this may lead to poor customer satisfaction and loyalty. Systems become unstable, difficult to maintain, and improve.

Factors to consider when choosing whether to use Agentic AI or RPA

• Complexity of processes – RPA is more suitable for defined processes with high repetition. Use agentic AI when the process is dynamic and decision-based.
• Type of Data – RPA does better with structured and stable data. While AI wins at natural language processing, image recognition, and unstructured data.
• Cost – Cheaper to integrate is clearly the RPA. However, while being more expensive, AI allows for expansion and flexibility.

Depending on your organization, other factors may shift your decision as well.

Will AI agents replace RPA?

In short, the answer is no!

AI agents are a long way from destroying RPA. For some time, they will operate together, and the RPA will be maintained on a large scale, with non-decision-making processes. Because of its stability and auditablility. As the AI agents progress, they will gradually replace RPA for more complex processes, where decisions are needed at any stage of the flow.

Facing the automation challenges

Success with Agentic AI depends on addressing challenges such as reliability, oversight, and transparency. At Maisa, we are building Digital Workers with accountability at their core. They are designed to reason clearly, act consistently, and explain their decisions. This way, businesses can scale automation with confidence.

RPA delivers fast, stable, rule-based processes, while Agentic AI opens the door to automating more complex and adaptive workflows. The change marks a move from following predefined scripts to achieving outcomes with autonomy.