Consider this analogy: RPA is like a train running on a fixed track, while Agentic AI is like an autonomous taxi, capable of navigating the unpredictable streets of a bustling city. Or to use another comparison, RPA is like using offline maps, whereas Agentic AI is like using Waze - dynamically adjusting to real-world conditions.
RPA is excellent for high-volume, repetitive tasks that follow a predictable pattern. It's akin to the robots in a factory, efficiently performing predefined functions as they move along fixed paths.
Agentic AI technology, on the other hand, is designed to constantly plan, execute and evaluate at every step to achieve a goal. It's more like a self-driving car, navigating complex, ever-changing environments and making real-time decisions. While RPA has its uses, the long-term potential of Agentic AI is exponentially greater.
Implementing RPA often involves extensive process mapping, with consultants and RPA engineers spending months automating workflows before they go live. If the underlying process changes, the RPA bot often breaks and needs reconfiguration.
Agentic AI, or in our parlance Composable AI Agents, learn directly from observing manual processes and workflows, observing their actions, asking questions, and adapting on the fly. It usually is operational within hours, not months, and effectively handles edge cases. This more organic, self-serve model upends many of the assumptions of the RPA industry.
Where RPA struggles, Agentic AI excels
Following is a non-exhaustive list of topics where we had first hand experience of RPA failures and shortcomings that we, like the rest of the industry, believe Agentic AI has the potential to offer comprehensive, previously unattainable solutions.
- Non-standardized processes: Processes that vary significantly between instances or require frequent human judgment
- Legacy systems: Older applications, especially those without modern APIs or with complex interfaces
- Unstructured data: Processes involving unstructured data or those requiring complex decision-making based
- Complex cognitive tasks: Tasks requiring advanced cognitive abilities, natural language understanding, or complex reasoning
- Dynamic web interfaces: Websites with frequently changing layouts or elements that load dynamically
- Frequent application updates: Target applications that undergo frequent updates or changes
With that in mind, let’s take a look at the main tradeoffs between RPA and Agentic AI.
| Capability | RPA | Agentic AI |
| Adaptability | Low - Follows fixed rules and scripts | High - Adapts to changing conditions |
| Learning Capability | Limited - Requires reprogramming | Continuous - Learns from interactions |
| Handling of Edge Cases | Poor - Often fails or requires human intervention | Excellent - Can reason through unique situations |
| Setup Time | Months of implementation | Hours to get started |
| Integration | Often siloed within specific applications | Seamlessly integrates across native systems, API or not |
| Task Complexity | Best for simple, repetitive tasks | Handles complex, reasoning-intensive tasks |
| Human Intervention | Frequently needed for exceptions | Can handle most situations autonomously, human supervises |
| Scalability | Limited by rigid programming | Highly scalable across various workflows |
| Impact on Workflow Efficiency | Incremental improvements in specific areas | Transformative impact across the organization |
Hopefully this will give you some new insights as you consider the best tool for your next business process optimization project. Please comment or send us feedback.
