How Agentic AI Goes Beyond RPA and Agentic Process Automation (APA) to Deliver Greater Value

Former Hewlett-Packard CEO Lew Platt famously said: “If HP knew what HP knows, we’d be three times more productive.”  Most organizations excel at capturing and gathering data. It’s only gotten cheaper and easier to collect mountains of information about existing and potential customers, but there are very few organizations that have articulated a strategy for making all of this data truly useful. 

Agentic AI is changing all of that. There are now ample opportunities for organizations to begin knowing what they actually know by connecting disparate systems and data sources behind a conversational interface. Imagine a smart speaker for a business where employees can connect to all the touchpoints across the company, including fellow employees and customers, by simply asking for assistance (or typing a request into a chat window).

Beyond a company knowing what it actually knows, agentic AI makes it possible to turn that knowledge into dynamic process automations that deliver massive return on investment (ROI). Organizations willing to take the plunge can create technology ecosystems where employees and customers can engage with an agentic operating system that’s connected to every data source and can interact with legacy software. This drastically enhances the quality of potential experiences in terms of personalization and efficiency. As such, it requires an approach that moves beyond traditional methods, such as Robotic Process Automation (RPA) and Agentic Process Automation (APA). Agentic AI is an orchestrated effort that’s also much larger than standalone Large Language Models (LLMs) and AI agents. 

A recent Forrester report [1] points out that, while stand-alone large language models (LLMs) can assist with summarization and Q&A tasks, “agentic AI systems can go much further: they can plan, decide, and act autonomously, orchestrating complex workflows with minimal human intervention. They can also access a variety of tools to accomplish their work, which gives them the ability to reach into the digital and, eventually, the physical world.”

RPA is good enough for high-volume, repetitive and structured tasks and while APA extends this to workflows with augmented LLMs for sem-dynamic operations, APA is still limited to tackling semi–dynamic workflows. Agentic AI on the other hand, leverages multi-model AI with LLMs, APIs for integration, and contextual understanding to tackle complex tasks autonomously. This extends to dynamic and non-linear workflows  requiring real-time decisioning. With proper configuration of LLMs and orchestration, agentic AI can tackle highly unstructured and complex tasks, which are beyond the scope of RPA and APA.

Automate Objectives, not Processes

Traditional approaches like Robotic Process Automation (RPA) have helped establish key drivers and strategies that bring value to agentic AI. In a typical customer order processing scenario, RPA can do things like extracting order information from emails or web forms, entering it into the system, updating inventory, generating invoices, and tracking orders. 

With the emergence of large language models (LLMs) RPA vendors are pivoting to Agentic Process Automation (APA), which uses LLMs to improve decision-making and execution capabilities in these kinds of automations. Still, APA lags behind Agentic AI in specific areas, such as non-linear workflows that require real-time decisioning. Despite its name, APA workflows are only semi-dynamic, requiring adjustments based on inputs. 

Truly agentic automations are flexible enough to execute highly-unstructured and complex tasks. An agentic system can collect the customer information listed above in any order across any channel — from the transcript of a voice call, an email, a text message, or a spreadsheet. If a customer phones a call center, an AI agent can begin the conversation armed with context. Behind the scenes, it can quickly review a summary of the customer’s recent interactions and ask them right away if they are calling to check the status on their most recent order. The system might also act as a “smart speaker” following the conversation in real-time and deploying an agent to feed a human representative relevant information about the caller via text. 

Rather than following predefined process automations and completing them in the same exact manner each time (as in RPA), if a user asks for help with something, an agentic system can disambiguate the request, determine the objective goal, and assemble an array of AI agents to automate a workflow that matches the specifics of the request. This gives organizations a higher degree of control over the quality, reliability, and adaptability of automations. 

Agentic AI Unlocks Knowledge and Flexibility

Agentic AI allows machines to understand, process, and respond to human language in a natural, conversational manner, bridging the gap between human communication and machine execution. This means that process automations no longer need to ride on brittle rails. Instead, they can adapt to individual situations and provide increasingly personalized interactions.

Automations at this level can be incredibly complex. Not only do agentic systems need to establish meaningful connections across an organization’s data sets, they also need to communicate with existing software. Agentic AI-led automation uses an orchestration layer to improve visibility, reduce complexity, and effectively manage agentic workflows. This gives organizations much greater control over the scalability, lifecycle, and execution of business processes automations. For any platform to enable this kind of orchestration it has to have the flexibility to plug and play with the best tools at any given moment as well as the openness to leverage existing systems and data sets.

While this level of automation requires a much heavier lift than RPA or APA, the inherent value of a system that can operate in this way is apparent. According to a report from Futurum Research [2], 89% of CIOs (chief information officers) consider agent-based AI a strategic priority, and agent-based AI will drive up to $6 trillion in economic value by 2028, accelerating AI’s role in automating enterprise workflows.

A New Class of Software Opens the Doors

Agentic AI-led automation is a fundamentally different way to think about software. Rather than writing out explicit instructions in an assortment of coding languages, people can talk to machines that can write software on the fly to complete their objectives. These are the kinds of automations that can always be learning from humans and from their own interactions. They are part of a system that knows what its organization knows and can be leveraged in an almost limitless number of ways.

According to Gartner [3], agentic AI will be autonomously resolving 80% of common customer service issues without human intervention by 2029. They estimate this will lead to a 30% reduction in operational costs, but these numbers will only be in reach for organizations that approach these technologies with a clear strategy and the right technology partners.

An emerging class of flexible orchestration platforms are enabling enterprises to connect multiple enterprise systems to automate and orchestrate end-to-end business processes.

OneReach.ai Agentic AI orchestration platform allows organizations to simplify processes, streamline AI agent deployment, and enable seamless interaction with necessary tools and systems. It empowers organizations to model, implement, operate, monitor, and optimize their long-running processes.

Sources:
[1] Forrester Trend Report 2025: Agentic AI Is Rising and Will Reshape Businesses That Embrace It

[2] Futurum Research Report: The Leading Solutions Transforming Enterprise Workflows in 2025

[3] Gartner predictions