Agentic AI with RPA: Overview, Pros and Cons

[SayeedBinAbdullah]

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Robotic Process Automation (RPA) has helped businesses by taking care of boring, repeated tasks-like handling invoices, filling out forms, or creating new user accounts. It works best when the task follows a clear set of steps that do not change much.

But in the real world, many tasks are more complicated. That is where Agentic AI (also called Agentic Automation) comes in place. This kind of AI not just responds to questions, but it can think ahead, plan what to do, and change its automation actions depending on what’s happening.

In this blog, we’ll explain what happens when you combine Agentic AI and RPA-the good parts, the problems, and what to keep in mind before using them together.

What is Agentic AI?

Agentic AI refers to artificial intelligence system that can autonomously plan, make decisions, and take actions to achieve goals with minimal or no human intervention. Unlike traditional AI, which typically reacts to specific inputs, agentic AI behaves more like an independent "agent" that:

• Sets and pursues goals dynamically
• Learns from the environment and adapt its actions
• Coordinates tasks across systems without the need of constant prompts

Instead of answering once and stopping, Agentic AI can:

• Keep track of what it has done and what is left
• Use other tools, like websites and various software APIs
• Auto retry and fix the issues if something goes wrong (For e.g. an element is not found in UI)
• Involve human user when needed for review

This is different from basic AI, which just gives a one-time answer without thinking ahead.

Why Use Agentic AI with RPA?

You can think of RPA as a static rule-based or fix decision based digital worker which follows instructions exactly as trained by the developer but doesn't think by itself. Agentic AI is like giving that digital worker a brain which brings understanding, flexibility, and decision-making.

When you combine both, you get a system that can handle both rule based as well as dynamic tasks.

Here’s how they support each other:

Feature	What RPA Does Well	What Agentic AI Adds	What They Do Together
Task Type	Handles simple, repeated steps	Adapts to changing situations	Can handle both types of tasks
Data Handling	Works with structured data like forms or Excel	Understands unstructured data like emails and various documents	Covers a wide range of data, interpret context and provide output
Decisions	Follows fix set of rules	Makes decisions based on context	Automates tasks with smart decisions
Integration	Works well with older and legacy systems	Connects with new tools and understands language	Bridges old and new systems

Example 1: A Smart Email Assistant

Imagine a company receives hundreds of emails each day toa shared inbox like support@yourcompany.com

• RPA can pull these emails from Outlook.
• Agentic AI can read each email, figure out the tone (angry, polite, neutral), and decide what to do, should it reply, send it to human for review, or trigger some automation based on email context.
• The AI can then write a contextual reply with the relevant details pulled from the backend systems. Also, the Agentic worker can get the email reviewed with human user if needed.

With this setup, you now have a digital support agent that works non-stop, does not forget details, and responds in a thoughtful way.

Example 2: Customer Care Agent

• An agentic AI in a customer support centre might monitor service requests, prioritize issues, assign agents, and escalate problems—all without being told what to do step by step.
• This use case can significantly enhance customer satisfaction and save valuable time for human support agents, who typically need to navigate multiple applications to resolve customer queries.

Benefits of Using Agentic AI with RPA

1. Smarter Automation
RPA is good with clear instructions but struggles with tasks that are unclear or open-ended. Agentic AI can understand the meaning behind what someone is saying, even if it's not written perfectly. For example, if a customer says, “I’ve been waiting forever for a refund,” Agentic AI can understand the frustration and reply politely, even though the sentence was not clear and checks the request status and trigger the refund automation in the backend if required.

2. Reduced Human Interaction
If something goes wrong in a normal RPA process, a person usually must step in. But Agentic AI can try again, ask for missing information, or try a different method to get the task done. This means less manual work for your team.

3. Improves Existing Bots
Agentic AI complements existing digital workers. You can add Agentic AI to help them do more advanced things-like write emails, check documents for errors, or make small decisions that normally needed a person.

4. Real-Time Thinking
Agentic AI can act based on live data. Let’s say a customer complaint:
"I’ve been waiting for my refund for three weeks!"

Your system can create a response like:
"We’re very sorry for the delay. We’ve checked your request and are processing your refund now."
Traditional RPA can’t do this kind of flexible, human-sounding response.

Cons of the Agentic AI

1. Varying Results
Agentic AI might not give the same answer every time, even if the input is the same. This is very different from RPA, which always does the same thing.

Tip: Use fixed settings and standard templates to make Agentic AI more consistent. Add a layer of governance and human control on the results. Then slowly as Agentic AI learns, the human involvement can be reduced.

2. Complex Troubleshooting and Recovery
When an RPA bot fails, you can usually find the issue in the logs. But with Agentic AI, you also need to check:

• What the instruction (prompt) was
• How the AI understood that instruction
• What tools it used and what reference data it referred to generate the output
• Whether it got stuck repeating the same step

This makes debugging more complex and time-consuming.

3. Security and Data Risks
Agentic AI can sometimes:

• Use the wrong words in a reply
• Accidentally include personal or sensitive information
• Connect to outside tools or services if not properly restricted

Tip: Set clear rules, limit what AI can access, and always put the data protection policies in place by agreement of all parties involved.

4. Cost and Maintenance
Building agentic AI systems requires significant investment in advanced hardware, data infrastructure, and specialized talent. Continuous monitoring, updating, and fine-tuning of autonomous agents can lead to substantial recurring costs.

Adapting existing systems to work with agentic AI may involve costly re-engineering and automation re-design to fit with existing RPA automations.

Tip: Plan a clear ROI strategy before investing in agentic AI to ensure long-term cost efficiency and scalability.

5. Team Upskilling
Developers may need to acquire new skills to effectively work with Agentic AI systems.

This includes the ability to:

• Prepare effective and precise prompts (AI instructions) as these directly influence the output.
• Monitoring and Governance of AI Agents, Performance and associated costs.
• Error Handling in Agentic AI Automations.
• Ethical and Responsible AI Use.

Tip: Start small and give your team proper training. Build a simple use case first before moving to more complex automation.

When to use Agentic AI with RPA?

Use it when:

• Your task has many "it depends" situations
• You work with unstructured data like PDFs, chats, or emails
• You want bots that can handle real-life conversations and scenarios

Avoid it if:

• Your task must always give the same result. Like a rule-based automation
• You work in a highly regulated area where AI flexibility can be risky
• Your team is not yet comfortable with using or managing AI
• Your automation really does not need the AI layer and works best with rule-driven approach

Conclusion

Agentic AI doesn’t replace RPA - it makes it better. It’s like giving your bots a brain. But just like people, that brain needs rules, training, and supervision.

If you're ready to guide and manage this setup properly, combining Agentic AI with RPA can help you automate tasks that were too tricky or too flexible in the past.

This kind of smart automation is still new. So, start small - choose a simple task where mistakes won’t cause big problems. Test it, learn from it, and improve as you go.

The future of automation is not just about doing things faster - it's about doing them smarter.

[Tejaskumar_Darji]

Thank you for sharing the details on the integration of Agentic platforms, as well as the advantages and disadvantages to keep in mind.

[faheemsd]

Thank you @SayeedBinAbdullah for providing valuable insights on AGenetic AI integrated with RPA.