One of my first jobs as a college graduate was writing Robotic Process Automation (RPA) workflows using Leapwork. I was able to automate several repetitive, time-consuming tasks, which gave me a deep appreciation for the power of engineering.
RPA involves using software “bots” to mimic human interactions with digital systems. These aren’t physical robots, but rather modular software programs designed to perform tasks like data entry or web scraping.
In traditional RPA every bot must be explicitly programmed and configured. Its actions are strictly rule-based, following a predefined script without any inherent intelligence. Your early chat bots (yes the frustrating ones 🙂) are prime example of RPA automation.
RPA naturally evolved into Intelligent Process Automation (IPA). This next step integrates the rule-based orchestration of RPA with Artificial Intelligence (AI) and Machine Learning (ML). This enhancement allows automation to handle more complex processes and work with unstructured data, moving beyond simple, rigid instructions. Tools like Grammerly were developed using this techinque which could read your email and suggest corrections
We are now entering the era of Agentic Automation. Autonomous agents capable of understanding context, making decisions, and adapting to changing environments. Unlike RPA or even IPA, these agents don’t just follow a script; they can reason, plan, and execute multi-step tasks to achieve a high-level goal, learning and adjusting as they go. Agents can now not only correct grammatical mistakes but also write whole articles for you. (I know what are you thinking 😉)
The rapid growth of the automation landscape is exciting. It’s tempting to discard RPA as a thing of the past, but that would be unwise. We are still in the very early stages of agentic automation, and building effective agents requires us to borrow heavily from the foundational principles of RPA.
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RPA forces you to break down a workflow into clear, logical, and repeatable steps. This disciplined approach is essential for training reliable AI agents and building multi-agent workflows.
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Automating processes generates structured data about how those processes work, identifying bottlenecks and inefficiencies. This data is the fuel for training smarter, more capable AI models.
Ultimately, the most efficient and cost-effective automation solutions won’t be about choosing one technology over another. True efficiency will come from orchestrating RPA, IPA, and agentic systems together, allowing each to handle the tasks it’s best suited for within a single, complex, and powerful workflow.