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Compare AI agents vs. RPA: Key differences and overlap

By Kashyap Kompella

Enterprises looking to automate their operations face a choice: Stick with reliable but constrained robotic process automation, or explore adaptive but experimental AI agents?

RPA is a tried-and-tested technology designed for automating specific, rule-based tasks. It excels at handling structured data and predefined workflows. Agentic AI is a newer automation approach that uses large language models and external tools. Unlike RPA bots, AI agents can perform tasks that involve unstructured data and that require flexibility and decision-making.

Although RPA is well established and provides stability, its capabilities are also more limited; AI agents, in contrast, are more versatile, but also more experimental and error-prone. And the choice isn't always either-or: As RPA platforms modernize and AI agents mature, the two technologies can complement each other, with RPA bridging legacy systems and AI-driven processes.

When to choose RPA bots vs. AI agents

Use RPA when a process is repetitive and rule-based, the inputs are structured and predictable, and no decision-making is required. Use AI agents when a task involves unstructured data, requires ongoing learning and adaptation, and involves making judgment calls.

RPA is most effective for enterprise use cases such as the following:

• Logging in to systems and moving data between applications.
• Extracting structured data from emails and entering it into databases.
• Processing documents that follow a fixed template, such as invoices.
• Updating records in legacy systems that lack APIs.

AI agents are best suited for enterprise use cases such as the following:

• Analyzing customer sentiment on social media.
• Responding to customer queries in a dynamic and unscripted way.
• Generating humanlike text for reports.
• Summarizing and extracting insights from unstructured data.

When does it make sense to use both RPA and AI agents?

Consider using RPA alongside agents when a process includes structured and unstructured elements, or when RPA can handle execution while AI manages analysis. Examples include the following:

• Data extraction. RPA handles structured documents, while AI agents process unstructured ones.
• Automated email responses. RPA manages templated replies, and AI agents personalize responses.
• Customer onboarding. RPA performs data entry, and AI agents make decisions based on customer information.
• Intelligent document processing. RPA extracts data, and AI agents interpret it.
• Automated IT support. RPA resets passwords, while AI agents diagnose and resolve more complex issues.
• Insurance claims processing. RPA manages structured workflows, while AI agents interpret complex documents.

Understanding RPA

RPA uses software bots to complete repetitive, structured tasks using set rules. These bots mimic what a human does on a computer, such as clicking, typing, copying, pasting and processing data. RPA can be GUI-based, where user interactions with software interfaces are recorded and replayed, or API-based, where a visual drag-and-drop editor is used to create automations with direct system integrations.

RPA bots can be attended, meaning that users monitor or interact with them, or unattended, meaning that they run in the background independently. In RPA, the process is defined in advance, and the bot follows the steps exactly as programmed. If something changes outside its predefined rules, it fails.

RPA platforms typically consist of tools to create automations, connectors to common enterprise systems, process mining and analysis utilities, prebuilt AI templates, and bot orchestration capabilities. Examples of RPA vendors include Automation Anywhere, EdgeVerve, IBM, Microsoft, Nice, Nintex, Pega, SAP, ServiceNow, SS&C Blue Prism, Tungsten Automation and UiPath.

Understanding AI agents

AI agents are automation programs powered by LLMs that can use external tools to complete tasks. They operate using an orchestration framework that guides them from receiving an initial prompt to using various tools accessible to the LLM to complete a task. Some AI agents are designed to interact with users, while others operate in a self-contained manner.

An AI agent begins to process a task when it receives instruction in the form of a prompt. The agent then uses an LLM to analyze the data and decide on an action to take based on learned patterns or reasoning. It then executes the action to complete the task. Examples of agentic actions include browsing the web, searching documents, running code, and making function or API calls.

Fully autonomous general AI agents -- agents with general intelligence that can independently reason and adapt across any domain -- remain hypothetical. Today's AI agents are narrow, meaning that they perform defined tasks within specific areas and involve some level of user guidance. So-called deep search or deep research tools, such as those launched by OpenAI, xAI and Perplexity, are examples of narrow agents: They can perform internet research and create detailed reports in response to high-level user requests.

Some LLMs now also feature "computer use" capabilities, which enable the LLM to operate the user's computer and perform tasks -- overlapping somewhat with GUI-based RPA functionality. Examples include Anthropic Computer Use, Google Mariner and OpenAI Operator.

AI agent orchestration frameworks help manage workflows and integrate AI agents with external tools. Examples include CrewAI, Google Vertex AI Agent Builder, Hugging Face Smolagents, Langflow, LangGraph, Letta, LlamaIndex, Microsoft AutoGen and Semantic Kernel, and OpenAI Swarm.

Factors to consider when deciding between RPA and AI agents

RPA and AI agents each come with pros and cons. The following are some of the most important considerations when choosing which technology to use.

• Maturity. RPA has been around for 15 years and is better established in the enterprise. AI agents are a more experimental technology that has existed for a much shorter time period, and there are few examples of large-scale business deployments.
• Versatility. RPA bots follow predefined scripts and are intended to execute existing processes more efficiently, easing capacity bottlenecks and automating business processes. AI agents are more versatile and can not only automate existing workflows, but also introduce new use cases.
• Adaptability. Unlike RPA bots, AI agents can learn over time, make judgments and call other tools without being explicitly programmed to do so. This enables them to adapt to new circumstances better than rule-based RPA bots, but also requires a careful analysis of potential security issues.
• Multimodality. AI agents can be multimodal, meaning that they can process multiple input types and generate output in multiple formats, such as text, audio, images and video. RPA bots are more limited in their input and output types.
• Compute footprint. AI agents can be quite computationally expensive, whereas RPA software is comparatively lightweight. AI agents can also introduce latency due to LLM inference time.
• Interface dependency. GUI-based RPA bots must be reprogrammed when software interfaces change. AI agents, in contrast, can adapt more flexibly to handle UI changes.
• User-facing roles. RPA bots mainly operate in the background or tend to be employee-facing. Although RPA bots can be customer-facing, AI agents are typically better suited for direct customer interactions.
• Reliability. AI agents' higher autonomy means that they can hallucinate and go off-track when attempting to complete a task, unlike RPA bots, which stick to the script. When deployed in external-facing scenarios, AI agents need more guardrails and oversight.
• Pace of innovation. The LLM space is rapidly evolving, with new companies, tools and features emerging regularly. RPA vendors are comparatively more stable and better established, with RPA platforms focusing more on using third-party AI capabilities within structured enterprise automation contexts.
• API access. AI agents depend on API availability for system access. In environments with limited API access, RPA can interact with UIs directly, where AI agents struggle.

Kashyap Kompella is an industry analyst, author, educator and AI adviser to leading companies and startups across the U.S., Europe and the Asia-Pacific region. Currently, he is the CEO of RPA2AI Research, a global technology industry analyst firm.