Agentic AI vs. RPA: What’s the Difference?

What Is the Difference Between RPA and Agentic AI?

The main difference between RPA and agentic AI is that RPA follows predefined rules to automate repetitive tasks, while agentic AI makes autonomous decisions, adapts to changing contexts, and pursues goals using reasoning.

RPA is static and task-based; agentic AI is dynamic and outcome-driven.

Agentic AI vs. RPA

Agentic AI:

• Goal-oriented and autonomous
• Can make decisions and adapt to new data
• Uses memory and context to act over time
• Handles unstructured or dynamic environments
• Requires more resources and oversight to implement
• Often relies on large language models (LLMs) or multi-agent systems
• Introduces new risks (such as model drift, hallucinations, and misalignment)

RPA:

• Rule-based and deterministic
• Executes predefined scripts or workflows
• Optimized for repetitive processes and structured tasks
• Works best in stable and predictable environments
• Quick to deploy and integrate with legacy systems
• Requires minimal AI/machine learning infrastructure
• Low risk when properly maintained

Key Differences Between RPA and AI Agents

While RPA and Agentic AI are both forms of automation, they operate on different principles.

Confusing the differences can lead to compliance risks in highly regulated industries like finance and insurance, mismatched tools, and wasted budgets.

Therefore, these are the key differences between RPA and artificial intelligence agents to pay attention to.

Core Principle

Agentic AI systems operate with autonomy, which means that they make decisions, adapt to context, and go after goals without step-by-step instructions.

Agentic AI example: In insurance underwriting, an AI agent can review customer data, analyze risk, review existing internal data, and recommend a personalized policy for the customer.

On the other hand, RPA is rule-based and deterministic, which means it only executes complex business processes in the exact same way each time.

RPA example: Automating data entry from claim forms into a core insurance platform.

Task Type

AI agents are suited for complex, dynamic, and unstructured task types that require reasoning or context-awareness.

‍Agentic AI example: An AI agent that reviews incoming insurance claims, cross-references them with policy documents, and extracts key details from unstructured documents.

RPA excels at structured and repetitive tasks, taking advantage of clearly defined steps.

RPA example: Extracting customer account balances from a banking system every day at a set time.

Learning Ability

The greatest thing about Agentic AI is that it can improve and learn over time through reinforcement learning, memory storage, and fine-tuning.

‍Agentic AI example: A customer service AI learns better response strategies from past interactions.

RPA doesn’t have learning capability, and it only does exactly what it’s told every time.

RPA example: RPA can’t adapt if the layout of a web form changes unless the developer updates the script.

Human Supervision

Agentic AI is best when it operates semi-autonomously, including human employees for more complex tasks or high-risk decisions that often involve compliance and ethical alignment.

Agentic AI example: In banking, AI agents making loan approval decisions should be overseen in complex processes or high-stakes situations to ensure proper judgment.

RPA requires minimal supervision as long as the process hasn’t changed.

RPA example: When you program a regulatory report generation task, it can run on schedule with minimal checks.

Technologies Used

Agentic AI uses a variety of technologies, including LLMs, memory modules, planners, vector databases, and orchestration tools.

Agentic AI examples: A financial AI agent that autonomously gathers market intelligence, summarizes earning reports, stores context in memory, and suggests investment strategies tailored to client portfolios.

RPA relies on UI automation, workflow engines, and scripting languages.

RPA examples: Tools like UiPath or Automation Anywhere for automating repetitive HR tasks.

Typical Use Cases

Agentic AI can complete tasks that require reasoning, goal-setting, or context-awareness across multiple steps or systems.

It’s ideal for situations where decision-making evolves with new information.

Ideal use cases for agentic AI: Automating insurance claims triage by interpreting unstructured claim documents, conducting continuous compliance monitoring by scanning regulatory updates, or managing customer service agents that adapt responses based on previous interactions.

RPA excels at automating well-defined and repeatable tasks with structured inputs and stable environments.

The ideal use cases for RPA include copying data from PDFs into enterprise systems, automating monthly payroll calculations, and filling regulatory forms across internal systems.

Scalability

Agentic AI scales by learning, generalizing across tasks, and handling inputs, which also includes retraining, fine-tuning, and governance.

Agentic AI example: An AI support agent trained on company data can expand to handle new product lines without manual rule updates.

RPA scales linearly, so each new task requires building a new bot or modifying existing scripts.

RPA example: If each department in a bank needs slightly different reporting logic, you may need to separate RPA workflows for each one.

Reliability

Agentic AI can be powerful but unpredictable, especially under weak supervision. It may hallucinate or misinterpret context without strong constraints.

Agentic AI example: An AI summarizing medical records might omit critical symptoms if not fine-tuned properly or if input formats change.

RPA is highly reliable when operating in a stable environment with clearly defined rules.

RPA example: An RPA bot that logs into a billing system daily to download transaction reports will function consistently, as long as the UI doesn’t change.

Implementation

Agentic AI requires a more complex setup, involving model integration, orchestration, testing, and compliance alignment. It’s a strategic investment, not a quick win.

Agentic AI example: Deploying a multi-agent system to automate insurance claims adjudication could take months, requiring collaboration between IT, legal, and compliance teams.

RPA implementation is faster and less resource-intensive, making the process suitable for teams needing immediate gains.

RPA example: Automating invoice forwarding in an ERP system could be completed in a few days using off-the-shelf RPA solutions.

How to Choose Between RPA and Agentic AI

The right tool depends on the type of work you’re automating, but also on the complexity of the environment.

Choose RPA if:

• Your process is rule-based, repetitive, and works with structured data
• You need fast, low-cost automation with minimal oversight
• Your systems and workflows don’t change often
• You’re working in legacy environments where scripted interaction is reliable

Choose agentic AI if:

• Your workflow involves judgment, decision-making, or adapting to new information
• You’re dealing with unstructured data, like PDFs, emails, contracts, or web content
• The process spans multiple steps, tools, or departments
• You want the system to learn and improve over time
• You need autonomous agents that work toward an outcome, not just tasks

For example, if you’re trying to extract, understand, and act on insights from thousands of loan applications or policy documents, RPA would fall short.

That’s where AI solutions like Unstructured AI come in. This AI Agent can parse unstructured inputs, enrich them with context, and automatically feed them into downstream workflows.

Therefore, it’s not just data-parsing, it’s decision-ready data activation.

Similarly, if you’re automating research-heavy tasks, a conversational workflow, or anything with open-ended inputs, you’ll need an agentic AI approach that can plan, reflect, and adapt, rather than just follow steps.

While RPA is great for automating knowns, agentic AI is better when you’re dealing with unknowns, edge cases, or goals that require intelligence and initiative.

Consider Combining RPA With Agentic AI

The great thing is that you don’t always have to choose one over the other.

Some of the most efficient process automation strategies come from combining RPA with agentic AI, especially in complex and highly regulated environments.

RPA handles the predictable, while agentic AI handles the unpredictable. Together, they improve operational efficiency that blends speed with intelligence.

Therefore, it’s best to combine RPA with agentic AI for:

• Intelligent document processing - Use RPA to retrieve documents from legacy systems, pass the files to AI agents to extract insights, classify content, or flag anomalies. Then use RPA again to route results into downstream systems.
• Customer onboarding - RPA can fill out forms and update CRM entries, while AI agents can analyze uploaded documents, assess risk, and provide tailored next steps.
• Compliance and audit trails - Agentic AI can monitor policies and flag potential violations across unstructured text sources, while RPA can log those findings, notify stakeholders, and store a record for future audits.

And if you’re thinking of combining them successfully, here’s how to do it:

It’s best to use RPA for deterministic tasks and use agentic AI for interpretive or dynamic ones, with both systems having clear guidelines on when to hand off to the other.

Connecting them together using REST APIs ensures smooth communication across platforms at all times.

Taking advantage of agentic AI platforms like AgentFlow can help manage tasks, retries, and fallbacks between systems.

But when you’re mixing RPA and AI, monitor both flows. It’s important to track inputs, outputs, failures, and human overrides to stay compliant and mitigate risk.

Lastly, piloting a single use case, such as document ingestion plus AI extraction, before expanding to other tasks when stable, is the smartest way to implement it.

Blending RPA’s precision and agentic AI’s adaptability gives you a hybrid automation system that’s both scalable and intelligent.

Implement Agentic AI In Your Workflows

Move beyond rigid scripts with intelligent agents that learn, adapt, and drive outcomes. Whether you’re looking to automate your existing workflows or combine RPA with agentic AI, we can help you implement agentic AI in 90 days or less.

Book a demo with our experts to see how AgentFlow works live and how it can help your business future-proof your automation efforts.