RPA vs. CUA: Which Automation Approach is Right for Your Business?

Automation is no longer a niche experiment, it’s fast becoming a baseline for business efficiency and innovation. In fact, companies that fail to automate repetitive tasks risk falling behind. A recent Salesforce survey found that 74% of employees using automation say it helps them work faster. . This surge in productivity explains why organizations worldwide are embracing automation, especially in back-office processes like finance, HR, and operations. One analysis found that up to 80% of finance departments’ transactional work could be automated​ which highlights the huge efficiency gains possible.

Modern automation comes in many forms. Two prominent approaches are Robotic Process Automation (RPA) and emerging AI-driven agents like Microsoft’s Computer-Using Agent (CUA) (supported by its new Responses API). Business leaders are hearing these buzzwords more often as they seek ways to streamline workflows. But what do they mean, and how do they differ? This article demystifies RPA, both attended and unattended modes. We will explore how Microsoft’s CUA and Responses API are opening new frontiers. We’ll also look at where these solutions overlap, when to use one versus the other (or both), and how we are helping organizations navigate this landscape.

Understanding RPA

Robotic Process Automation (RPA) refers to software “bots” that mimic human actions on computers to perform repetitive tasks. RPA excels at tasks that are rule-based and structured—precisely the kind of work that doesn’t require human judgment or creativity. As one definition puts it, RPA is software used to automate “repetitive, rule-based tasks – like moving files and folders or extracting data from documents”​. Rather than leaving these mind-numbing duties to people, RPA bots can do them faster, 24/7, and without mistakes. The result is often a dramatic improvement in process cycle times and accuracy.

One of the key distinctions in RPA deployments is whether the bots are attended or unattended:

• Attended RPA bots work alongside humans, on the same workstation a person is using. They are typically triggered by the user and handle tasks that need to happen in real-time, often in response to a customer interaction or an employee’s request. For instance, a call center agent might click a button to invoke an attended bot that pulls up a customer’s information from several systems, saving the agent from manual lookup. Attended automation is human-initiated and may involve human input during execution​. The bot essentially acts as an on-demand assistant, boosting individual productivity.

• Unattended RPA bots run in the background on servers or virtual machines, without human involvement. These bots are triggered by schedules or business rules and carry out entire processes autonomously. Unattended automation is ideal for high-volume, batch-oriented tasks—think nightly processing of payroll, or continuously monitoring an inbox and entering incoming orders into an ERP system. In unattended scenarios, no human interaction is required once the process starts​. The RPA system can even handle logging into applications with preset credentials to perform tasks at any hour. Essentially, unattended bots are the “digital workforce” toiling behind the scenes.

Where RPA Excels

RPA’s sweet spot is well-defined, repetitive processes with structured data. RPA brings significant speed and accuracy to routine work. Bots can operate 24/7 and complete tasks in a fraction of the time humans would, dramatically shrinking process cycle times. They also follow rules exactly, eliminating typos and inconsistencies—ensuring high compliance and quality​. RPA bots scale easily to handle growing workloads, and they integrate non-invasively by using existing application interfaces (no need for deep system changes)​.

RPA adoption is now widespread across industries. In finance, for instance, RPA bots are handling tasks like invoice processing, expense reconciliation, and financial reporting – significantly reducing manual effort. HR departments deploy bots to scan resumes, onboard new hires, and update employee records. In customer service, RPA helps transfer data between systems and even draft routine responses. Supply chain teams use RPA for order tracking, inventory updates, and shipment scheduling​. Virtually any routine, high-volume process can be a candidate for RPA.

However, traditional RPA is not a silver bullet for all scenarios. Bots can be brittle, if an application’s interface changes or a process deviates from the expected rules, the automation might fail. RPA also struggles with unstructured inputs (like free-form emails or images) and doesn’t “learn” from new examples; it does only what it’s explicitly programmed to do. To handle more dynamic or cognitive tasks, we need a smarter approach. This is where AI-based solutions enter the picture.

Enter Microsoft’s CUA and Responses API: AI-Powered Agents

Microsoft has recently introduced a technology in the automation arena: the Computer-Using Agent (CUA), along with a companion Responses API. These tools are part of Microsoft’s Azure AI platform (specifically Azure AI Foundry) and represent a leap beyond traditional RPA. They enable what Microsoft calls “agentic AI” – in simple terms, AI agents that don’t just chat or analyze, but can take actions in software like a human would.

So, what exactly is CUA? The Computer-Using Agent is essentially an AI model that interacts with graphical user interfaces (GUIs) and applications through natural language instructions​. In other words, you can tell this AI agent what task to do (in plain English), and it will figure out how to click, type, navigate, and execute that task across one or more applications. This is very different from RPA’s approach of following pre-scripted steps. CUA brings adaptive intelligence to automation: it can “see” what’s on the screen, interpret visual elements (buttons, links, fields), and dynamically decide how to complete the task.

The Responses API, on the other hand, is a powerful new interface that allows developers to incorporate such AI agents into workflows more easily. Think of the Responses API as the orchestration layer for AI-driven actions. It lets an AI system maintain context across multiple steps and invoke various tools as needed – for example, calling the CUA to perform a UI action or fetching data from a document, all in one structured response​. By combining multiple capabilities (chat comprehension, function calls, web searches, UI automation) into a single API call, the Responses API simplifies the integration of advanced AI into business processes. For a business leader, the technical details may not matter, but the outcome does: it means smarter bots that can handle more complex tasks seamlessly.

How CUA Differs from RPA

It’s helpful to contrast CUA with traditional RPA to understand when this new approach shines. RPA is rule-based, requiring explicit step-by-step instructions and often struggling when something unexpected occurs​. In contrast, CUA is AI-driven and context-aware. Key differences include:

• Autonomous UI navigation – CUA interacts with GUIs by opening applications, clicking buttons, and filling forms much like a human would​.
• Dynamic adaptation – It adjusts to changes in on-screen layout (unlike rigid scripts), so minor interface updates won’t derail the process​.
• Cross-application operation – CUA can seamlessly work across multiple desktop or web applications in one workflow, even if those apps aren’t integrated​.
• Natural language commands – Instead of code, users can instruct it in plain English and the AI will figure out the steps to complete the task​.

In essence, CUA brings cognitive capabilities to automation. It’s like an employee who can be told, “Do this task,” and will figure out the best way to do it by interacting with the same software tools your team uses. Under the hood, it leverages advanced models (from Azure OpenAI Service) to interpret instructions and drive the computer interface. This approach is part of a broader trend of intelligent automation – combining AI with automation to handle tasks that previously required human intuition.

When to Use RPA vs. CUA

• Structured, stable processes – RPA is the go-to for repetitive workflows with clear, unchanging rules. It’s a cost-effective, proven choice for tasks like pulling monthly reports or transferring data between two systems on a schedule.
• Dynamic or unpredictable tasks – If a process involves unstructured inputs (free-form text, images) or frequently changing steps, an AI-driven agent like CUA will handle it better. CUA can interpret context and adapt to those “corner cases” that would cause a rigid RPA bot to fail.
• User-driven interactions – For scenarios where employees or customers trigger automation in real-time via natural language (e.g. asking a chatbot to perform an action), CUA is ideal. It’s built for interactive use on demand, whereas RPA bots usually run on pre-set triggers or schedules.

It’s not always an either-or choice. RPA and CUA can complement each other within the same automation strategy. Microsoft’s vision with Azure AI Foundry is precisely to blend these: an AI agent can orchestrate RPA bots for parts of the workflow that are repetitive. In fact, Microsoft’s Responses API allows the AI agent to call various tools (including RPA actions) as part of its operation​. We may see scenarios where a CUA agent handles the high-level decision-making and UI interaction, and when it needs to do a bulk transaction entry, it triggers an unattended RPA bot to speed through that sub-task.

For business leaders, the takeaway is that automation is expanding its reach. Tasks that were once beyond the scope of automation—because they were too variable or required reading a screen—are now within reach of AI agents like CUA. But RPA remains a workhorse for the countless routine tasks that keep the business running. The smartest strategies use each tool where it fits best.

In commercial real estate, brokers often spend days collecting property information from various sources and updating multiple systems. RobinRocks – a prop-tech startup –is tackling this problem by creating a smart AI assistant to take over all that research and data entry. Estate Agents are losing hours searching for the right data, entering data into CRM systems and switching between different sources​. Now, the AI assistant automatically pulls rental and sale prices, ownership records, permits, and neighborhood details from online databases and populates the company’s CRM in real-time. A task that once took an agent two days of manual work can be done in just a few minutes, with the human broker only stepping in to review the compiled info. This means agents spend far less time on paperwork and much more on high-value activities like client relationships and closing deals.

Digital Bricks is helping organizations across industries turn automation ambitions into reality. As specialists in Microsoft’s AI and automation stack, we leverage tools like Copilot Studio, Azure AI Foundry, and the Power Platform to craft solutions tailored to each client. They begin by pinpointing the processes that will benefit most from automation, then design the optimal mix of technologies – whether that’s a straightforward RPA bot or a custom AI Copilot – to streamline those workflows. In many cases, the team combines both RPA and AI, for example deploying an AI agent to handle complex decision-making while using unattended RPA bots for high-volume routine tasks in the background.

Equally important, we deliver solutions that are cost-effective and culturally aligned with the organization. That means implementing automation in a way that fits the company’s budget and IT environment, and helping the human team embrace their new digital “coworkers.” By focusing on user training and change management, We ensure that bots and AI agents augment the workforce rather than disrupt it. The result is high adoption, fast ROI, and a smoother transition to an AI-assisted workplace.

The world of automation is evolving rapidly, and business leaders no longer have to choose between speed and intelligence – the latest solutions offer both. The key is to select the right tool for each job, and often a blend of the two. With an experienced partner to guide the way, companies can deploy these technologies to dramatically boost efficiency and innovation.

Ready to transform? Contact us today.