Where RPA bots require a hard set of pre-determined rules at every step along a task, Agentic AI requires a goal and figures out the rest.
Breaking the rules
Once ground-breaking and cutting-edge technology, robotic process automation (RPA) has reached the end of the line on Gartner’s hype-cycle. Today, RPA remains a staple investment to automate highly repetitive, structured, and rules-based tasks. But what happens when the rules go out the window? Let’s talk Agentic AI.
What is Agentic AI?
Agentic AI is the next leap for artificial intelligence technology making its ascent to the top of the hype cycle in 2025. Agentic AI, also called an AI agent, shifts the focus away from prompt-induced content generation AI and towards goal-driven decision-making. It is a software entity that combines RPA task automation with the dynamic mental capabilities of artificial intelligence to proactively and autonomously perceive, make decisions, and take action.
For an extreme oversimplification — where RPA bots require a hard set of pre-determined rules at every step along a task, Agentic AI requires a goal and figures out the rest.
How does Agentic AI differ itself from AI noise?
AI has been talked about exhaustively in the last few years with topics ranging from generative AI, large-language models, chatbots, and more. So where does Agentic AI fit in this ecosystem of AI buzzwords?
Agentic AI is its own software entity. It is not a type of language model — nor can it be characterized by a single set of rules, procedures, processes, interfaces, chatbots, or otherwise. Instead, AI agents wield any and many combinations of these AI tactics and techniques to carry out end-to-end tasks.
The new digital workforce
Forever, we’ve called RPA bots part of a “digital workforce”. However, these digital worker bots could only execute simple tasks that required a good amount of human supervision, particularly if they encountered roadblocks. Imagine then that this digital workforce has, in a sense, significantly upskilled to handle even greater complexity, adapt to changes, and deliver human-like activity autonomously.
And all that upskilling has come with a significant pay bump for AI. UiPath predicts…
The compound annual growth rate (CAGR) of Agentic AI is around 29%, with a potential of hitting 4.1 billion in investments by 2028.
So what are the benefits?
We haven’t seen AI agents deployed in their entirety yet but we can surmise a handful of benefits:
- Enhanced Decision-Making: AI agents can analyze complex data sets to make informed decisions without human intervention.
- Scalability: Agentic systems can manage a wide range of tasks, from simple to complex, across various domains.
- Adaptability: Agentic AI adjusts to new information and changing environments, ensuring continuous improvement and machine learning.
- Greater collaboration: AI agents enhance human performance and engagement by integrating and collaborating between existing systems and separate processes.
- Improved customer experiences: Agentic AI could directly benefit customer interactions by their customizable and predictive nature – inferring customer intent, predicting needs, and offering tailored solutions.
- Efficiency: By automating complex tasks, businesses can achieve higher productivity and reduce operational costs.
AI agents aren't coming for your job
The big questions that always comes up when we talk automation and reducing operational costs — if AI agents can autonomously and proactively perform human actions, is it the last straw for humans or the beginning of the end? We’re going to confidently say “no” to both.
Simply put, Agentic AI is a highly technical piece of software — not a colleague. Humans are the necessary link in a symbiotic relationship between bots and AI, required to provide guidance to AI systems, train and tweak performance, and ensure accuracy and compliance.
AI safety remains a fundamental business goal in 2025. And in the same way that replacing your content writers with ChatGPT ain’t gonna work (sorry Charlie, I’m still click-clackin away while you’re busy tripping on strawberries), AI agents will still make mistakes.
Minimizing these mistakes requires skilled individuals with an understanding of AI systems to set testing environments and improve the models the agents run on. And equally important to the quality of the model is the quality of data fueling the model, as well as how much access and autonomy AI agents are allowed to have to that data.
Humans are fundamentally necessary to manage data and determine data access level, ensuring AI agents aren’t exposing sensitive information or operating on hallucinations.
How can I get some of that AI?
Many former RPA-focused vendors are in a bit of an arms race to weave AI and RPA together in a holistic platform for total automation and Agentic AI. UiPath, once all about RPA, has changed their official logo to include “Agentic automation” as part of their orange word mark. You might encounter Agentic AI through these types of automation platforms; notice more information on the topic coming from tech giants like Microsoft or Oracle; or determine to build your own AI agent.
But as with any hot-topic trending tech, we recommend a moment’s pause. Before diving into the deep end of Agentic AI, please ensure there’s careful consideration and a strategic plan for your agentic project.
The aforementioned UiPath has an extremely extensive guide regarding everything there could be to know about AI agents, and as part of that guide they’ve included some best practices for agentic implementation, including:
- Ensure strong governance frameworks and compliance measures.
- Implement advanced security measures and protocols, as well as ensure data compliance.
- Conduct rigorous testing and validation in various scenarios. (Synthetic data is your friend here).
- Set a plan in place for regular update management and to ensure security protocols are being adhered to.
Ensure AI readiness
We’d add “ensure your data is AI ready” to the list above — a topic we touched on in a previous blog. To recap here, any AI system is going to require AI ready data, processes, and people to discourage hallucinations, train and test models, and enable systems that are actionable, reliable, and compliant.
People can be upskilled or hired with the necessary skills to work with AI, and hardware/software can be purchased and implemented — but improving data quality is often overlooked. Ensuring data is accurate, complete, and available is essential. In some cases, that’s as simple as removing old, duplicate, or inaccurate files. Other times, intelligent capture systems are required to bring unstructured or static structured data from documents and files into your content repository. The best time to start that process was yesterday. The second best time is today.
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