RPA vs. AI Agents: Differences and When to Use Each

RPA performs repetitive tasks by following predefined rules. An AI agent interprets context and determines the next step within established boundaries. The choice depends on the type of problem: structured, predictable tasks can be handled by RPA, while activities that require selecting and adjusting actions based on context may benefit from AI agents. In practice, both technologies can work together within the same process.

What Is RPA?

RPA (Robotic Process Automation) uses software robots to replicate repetitive human actions across systems, following predefined rules. UiPath, a Smarthis technology partner and one of the leading RPA providers in the market, describes the technology as the use of "software robots to automate repetitive, rule-based tasks."

An RPA robot executes programmed logic, which may include conditions, alternative paths, validations, and predefined exception handling.

What Is an AI Agent?

An AI agent is a component capable of interpreting an objective, analyzing available context, making decisions within defined boundaries, and taking action through tools and systems, with a level of autonomy and oversight determined by the risk associated with the activity.

Unlike a workflow based solely on predefined rules, an agent can select and adjust its next steps based on context, such as deciding which exception should be escalated to a person or how to handle a request that does not fit a fixed pattern.

How RPA and AI Agents Complement Each Other

UiPath reinforces this point by presenting RPA as an important capability within agentic automation. Agents can work alongside robots to perform repetitive, rule-based activities within a process.

In this combination, the AI agent can plan and determine the next steps, while RPA executes programmed actions across the company's systems. The agent can also interact directly with APIs and other tools, depending on the solution's architecture.

Smarthis specializes in automation and AI agents and maintains a Diamond-level partnership with UiPath. This expertise enables Smarthis to evaluate and combine technologies according to each client's needs, considering the systems involved, governance requirements, and expected outcomes.

When to Use RPA

RPA works well when a task is structured, repetitive, and can be performed using predefined rules: migrating data between systems, completing standardized forms, reconciling records with fixed formats, and extracting data from documents with predictable layouts.

The workflow may include alternative paths and predefined exception handling. Under these conditions, RPA can provide efficient and reliable execution. The decision should also consider whether an API, integration, or native system functionality would better meet the requirement.

When to Use an AI Agent

An AI agent is appropriate when a task requires interpreting context and choosing the next actions: triaging tickets received in different formats, analyzing a non-standardized document to determine next steps, deciding which path to follow within a process involving multiple possible exceptions, or responding to a request that does not have a single correct way to be handled.

In these cases, RPA can handle predefined exceptions, while the agent can help manage variations not covered by fixed rules, operating within defined boundaries and with human review when necessary.

Not every task that requires interpretation needs an agent. In some cases, an AI model applied to a specific step in a predefined workflow is sufficient, such as classifying a document or extracting information.

When to Use Both Together

Processes that combine structured steps with context-dependent decisions may benefit from both technologies: RPA executes rule-based activities, while the AI agent operates where it is necessary to assess the situation and determine the next steps, such as deciding whether an exception can proceed automatically or should be escalated for human review.

This combination often requires a coordination layer between the technologies, including triggering rules, execution monitoring, and failure handling. We have previously addressed this need in our article on why isolated automation is no longer enough.

Frequently Asked Questions

Will RPA Be Replaced by AI Agents?

Not across the board. RPA remains a relevant option for automating structured, repetitive tasks. AI agents expand automation capabilities to activities that require contextual interpretation and adaptive actions. Depending on the process, the technologies can work together or be evaluated as alternatives for a specific step.

Is an AI Agent More Expensive Than an RPA Robot?

There is no single answer. An agent may involve calls to AI models during each execution, but the total cost of either solution depends on factors such as licensing, infrastructure, development, volume, maintenance, and human review. The comparison should consider both cost and expected outcomes for each application.

My Company Already Uses RPA. Do I Need to Replace It With AI Agents?

Not necessarily. If existing automated processes continue to be effectively handled by RPA, there is no need to replace it simply to adopt agents. AI agents can complement existing operations in steps that require contextual analysis and adaptation of subsequent actions, provided they deliver benefits compared to simpler solutions.

How Do I Know Whether a Process Needs RPA, an AI Agent, or Both?

Map the process and identify which steps can be executed using predefined rules and which require interpreting context to select and adjust subsequent actions. The former may be handled through RPA, APIs, or other forms of automation. For the latter, assess whether applying AI to a specific task is sufficient or whether an agent is needed. When a process involves both requirements, the technologies can be coordinated within a single workflow.

Talk to a Smarthis specialist to identify where RPA, AI agents, or a combination of both would be most appropriate for your operations.