by Kalpan Vaghela, on Feb 28, 2025 6:10:57 PM
Key takeaways from the blog
- Technologies, such as RPA, are still very much in use and relevant.
- Agentic AI uses erstwhile automation capabilities holistically.
- Transition to Agentic AI enables businesses to achieve higher levels of speed and productivity.
As the market requirements shift dynamically, automation solutions, which sufficed till yesterday, fail to support business requirements today. However, businesses that have invested heavily in automation solutions feel discouraged to leverage new technologies that can fulfill the new requirements. The fear of increasing the IT-spend on technologies that become obsolete with each passing day is more than the fear of missing out or FOMO on the evolving technologies. In such cases, businesses should rest assured that the automation continuum always uses the earlier technology investments as a springboard to launch higher automation initiatives for productivity gains. Artificial Intelligence, which has already been around for some time, is such a springboard that launches Agentic AI, the latest in the automation continuum. Agentic AI leverages your data and intelligently orchestrates the existing automation investments to achieve higher levels of speed and productivity, which was unimaginable in the past, with increasing levels of autonomy.
Is Robotic Process Automation obsolete with the emergence of Agentic AI?
Robotic Process Automation (RPA), a rule-based automation technology, is still relevant for processes that require human intervention. The distance between RPA and Agentic AI is traversed using a series of supervised AI-models that exhibit increasing degrees of AI-led Intelligent Automation. Both Intelligent Automation (IA) and Agentic AI make holistic use of the rule-based RPA to start where the RPA purview ends. In other words, both IA and Agentic AI intelligently orchestrate RPA bots (and other automation solutions such as workflows and API integrations) to achieve pre-defined goals with an increasing degree of autonomy, respectively. As such, RPA is very much in use and is leveraged by modern automation solutions to automate more complex scenarios.
What are the steps for migrating from RPA to Agentic AI?
The primary difference between RPA and Agentic AI is that Agentic AI leverages both structured and unstructured data (along with existing automation solutions, such as workflows) to make autonomous decisions to achieve a predefined goal. Whereas RPA requires human intervention at each decision-making node. Agentic AI feeds on data inputs from the environment at run-time and adapts to the changing working conditions by adjusting the workload along dynamic workflows. So, the RPA to Agentic AI migration starts with having a well-structured data extraction and data management backbone.
The migration from RPA to Agentic AI includes the following main steps -
- Understand your data sources: The business should analyze the data required to run the Agentic AI workflows. They should curate the structured and unstructured data to train the AI-models. Well-trained AI-models form the basis of Agentic AI.
- Identify existing automation tools: Create a list of the incumbent automation solutions, which would be useful for the new initiative. The AI-models that underpin the Agentic AI should be trained to leverage these automation solutions to achieve the required outcomes.
- List the strengths of automation tools: The incumbent automation solutions have their own strengths. The business should integrate these capabilities with the Agentic AI framework to achieve the desired outcome.
- Select the function-specific AI Agents: The business should then select the required AI Agent, powered by the specific AI-model, to achieve the desired goal by orchestrating the selected automation capabilities along the pre-defined framework.
- Select APIs: Next, the business should select the APIs to integrate the AI Agent framework with the different automation capabilities to achieve the required target. Agentic AI can automate complex scenarios across siloed infrastructures and automation solutions using APIs.
- Leverage data: Use data to let the Agentic AI ensemble continuously interact with its ecosystem at run-time to achieve desired results.
- Integrate an orchestration layer: Agentic AI works in a constrained environment to achieve the desired result in the stipulated time. Hence, the business should establish a multi-agent framework that integrates with existing automation capabilities through an orchestration layer that propagates a two-way data exchange and interaction with its environment to achieve faster results.
- Institutionalize feedback loops: Agentic AI generates data with each interaction. The framework should have feedback loops that integrate the generated data with the data source that can be cumulatively leveraged to execute the next decision or AI Agent action.
Advantages of leveraging existing investments for Agentic AI
- Higher autonomy: It exhibits higher autonomy and quick decision-making in a secure and trusted environment.
- Better outcome: It achieves better and faster results. It acts in an unbiased environment based on the underpinning training models.
- Higher efficiency: It executes tasks with higher speed and accuracy than the earlier automation solutions to achieve the desired target.
- Adaptability: It continuously ingests the feedback generated while interacting with the environment to achieve desired scores, which are explainable, to feed the next decision-route.
Simply put
As technology matures along the automation continuum, the question arises whether the earlier automation investments have become obsolete. However, technology propagates by using the earlier automation as a springboard to launch itself. It uses the earlier automation capabilities as tools to navigate and chart the pre-defined goal by holistically using trained AI-models and data feedback loops at run-time. Agentic AI is one such technology in the automation continuum that uses erstwhile automation capabilities, such as RPA and workflows, to achieve higher speed and productivity with a greater degree of autonomy.
