Battle bots: RPA and agentic AI

I was recently speaking with a client who stated, “We’ve been doing RPA (robotic process automation) for years. What’s the difference with agentic AI? Should I really care, or is it all part of the GenAI hype?”

Valid question.

With the buzz around GenAI, it’s easy to see where this confusion and skepticism comes from. However, understanding the differences between RPA and agentic AI — and how they complement each other — can unlock major benefits through automation.

A key tool for organizations striving for an edge, automation has become a cornerstone of operational efficiency and innovation.

OK. So, what is the difference between RPA and agentic AI?

RPA refers to software tools designed to automate repetitive, rule-based tasks by mimicking human interactions with digital systems. It operates through predefined workflows, handling structured data in tasks such as data entry, invoice processing, and report generation. It’s particularly effective in industries like finance, healthcare, and logistics, where efficiency in routine processes is paramount.

Agentic AI, on the other hand, represents more capable autonomous decision-making, learning, and interaction. Unlike RPA, which executes static instructions, agentic AI adapts dynamically, processing unstructured data, analyzing context, and interacting conversationally with users —making it more suitable for complex problem-solving and decision-making scenarios.

Since people like lists, let’s differentiate the five key ways RPA and agentic AI differ, and then I’ll wrap up by discussing how they complement each other. It’s not a binary choice (RPA or agentic AI); it’s more blended.

Five key differences between RPA and agentic AI

Scope of automation

• RPA: Focuses on automating highly repetitive, rule-based tasks that mimic human actions. Examples include copying data between systems, generating invoices, processing claims, or assigning permissions when employees are hired or leave an organization. It operates within well-defined boundaries and workflows.
• Agentic AI: Expands beyond task automation to include decision-making, planning, and dynamic interactions. It excels in environments requiring analysis of unstructured data, such as customer support or supply chain optimization. Agentic AI can interpret nuances, learn from interactions, and adapt its behavior over time.

Flexibility and adaptability

• RPA: Operates on scripts and rules, making it rigid in adapting to new or unexpected scenarios. If an input or process deviates from predefined parameters, the system may fail or require manual intervention.
• Agentic AI: Exhibits flexibility by learning from data and interactions. It can analyze complex situations, infer context, and adjust workflows on the fly, making it resilient in dynamic environments. For instance, agentic AI could detect anomalies in a supply chain and recommend alternative routes without prior programming.

Integration and orchestration

• RPA: Integrates with existing systems through APIs or user interfaces but often requires significant setup and maintenance. It performs specific tasks in isolation, with limited orchestration across diverse platforms.
• Agentic AI: Acts as a connective layer across legacy and modern systems, orchestrating processes autonomously. It ensures smooth data flow and efficient operations by making intelligent decisions about which systems to engage and how. For example, an agentic AI system managing customer support could simultaneously pull data from a CRM system, a product database, and an ERP system to resolve complex customer queries.

Decision-making and context awareness

• RPA: Executes tasks based on fixed rules and predefined conditions. It lacks the ability to interpret broader contexts or make decisions beyond its programming.
• Agentic AI: Brings context awareness to automation. It analyzes intent, weighs multiple variables, and makes informed decisions, such as identifying fraud patterns in financial transactions or optimizing energy consumption in a smart grid.

User interaction and autonomy

• RPA: Typically requires human oversight to initiate tasks and address exceptions. Its role is that of a digital assistant, working alongside human operators to enhance productivity.
• Agentic AI: Can operate autonomously or engage users through conversational interfaces like chatbots. It provides a more interactive experience, collaborating with humans or independently performing tasks like conducting customer surveys or troubleshooting IT issues.

Complementary potential: Pairing RPA with agentic AI

While these differences highlight distinct strengths, RPA and agentic AI are not mutually exclusive. Pairing them can unlock additional levels of efficiency and effectiveness for organizations. Here’s how:

Enhanced workflow automation

RPA can handle straightforward, rule-based tasks, while agentic AI addresses complex decision-making and dynamic interactions. For instance, in a customer service scenario, RPA might extract and populate data from a CRM system, while agentic AI analyzes customer sentiment and provides tailored recommendations during live interactions.

Scalable error handling

RPA systems often struggle with exceptions or unstructured inputs. By integrating agentic AI, organizations can build systems capable of interpreting and resolving exceptions autonomously, reducing the need for manual intervention. For example, an RPA bot processing invoices might hand off unusual cases to an AI system for context-based analysis and resolution.

Dynamic adaptation in operations

Agentic AI’s adaptability can complement RPA’s precision. In supply chain management, RPA might execute routine inventory checks, while agentic AI adjusts procurement plans based on market trends, weather conditions, or geopolitical developments.

Enhanced customer experience

Combining RPA and agentic AI can elevate customer interactions. RPA automates back-end processes, such as retrieving account details, while agentic AI engages customers through personalized, conversational interfaces that anticipate needs and provide proactive solutions.

Intelligent orchestration across systems

In IT operations, RPA can perform tasks like logging incidents, while agentic AI correlates data from multiple sources to identify root causes and recommend resolutions. Combined, they enable a seamless, end-to-end automation ecosystem.

Conclusion: There is no “versus” for RPA and agentic AI

RPA and agentic AI are technologies that address different aspects of automation. RPA excels at optimizing repetitive, rule-based tasks; agentic AI has the potential to deliver value in environments demanding flexibility, decision-making, and context awareness. One is not innately better than the other. They have different limitations and complementary advantages.

By integrating RPA with agentic AI, organizations can build more robust, adaptive systems that combine the precision of rule-based automation with the intelligence and autonomy of AI.

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International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the technology markets. IDC is a wholly owned subsidiary of International Data Group (IDG Inc.), the world’s leading tech media, data, and marketing services company. Recently voted Analyst Firm of the Year for the third consecutive time, IDC’s Technology Leader Solutions provide you with expert guidance backed by our industry-leading research and advisory services, robust leadership and development programs, and best-in-class benchmarking and sourcing intelligence data from the industry’s most experienced advisors. Contact us today to learn more.

Daniel Saroff is group vice president of consulting and research at IDC, where he is a senior practitioner in the end-user consulting practice. This practice provides support to boards, business leaders, and technology executives in their efforts to architect, benchmark, and optimize their organization’s information technology. IDC’s end-user consulting practice utilizes IDC’s extensive international IT data library, robust research base, and tailored consulting solutions to deliver unique business value through IT acceleration, performance management, cost optimization, and contextualized benchmarking capabilities.