Agentic AI vs. RPA: Everything you need to know

Automation has evolved far beyond simple scripts and basic workflows. While robotic process automation (RPA) has long been used to handle repetitive, rules-based work, especially inside legacy systems, agentic AI represents a newer approach to automation built for far more dynamic problems.

Both are designed to reduce manual work and improve efficiency. But RPA works by mimicking human interactions with software through predefined rules and screen-based actions, while agentic AI systems are built to reason, plan, and adapt toward higher-level goals. Understanding how each works—and where each fits—can help you design automation that actually scales.

Here's everything you need to know about agentic AI and RPA. 

Table of contents: 

• Agentic AI vs. RPA: Overview 

• What is agentic AI?

• What is robotic process automation?

• Agentic AI vs. RPA: Similarities and differences 

• Agentic AI vs. RPA: Which should you use?

Agentic AI vs. RPA: Overview 

Agentic AI and robotic process automation (RPA) are both powerful ways to automate work, but they're built for very different kinds of problems. Here's a quick breakdown of the difference between the two. 

• Agentic AI is best used for complex, goal-driven workflows where systems need to reason, adapt, and decide what to do next—especially when inputs are unstructured, conditions change, and work spans multiple tools and systems.

• Robotic process automation (RPA) is best used for high-volume, repetitive tasks that follow clear rules and need to be executed the same way every time, especially in legacy systems or applications without APIs.

What is agentic AI?

Agentic AI is a system of AI agents that collaborate to achieve complex goals with minimal human intervention. Rather than simply responding to prompts or following rigid instructions, agentic AI systems can decide what actions to take, which tools to use, and how to adjust when circumstances change.

How does agentic AI work?

Agentic AI systems typically operate in a continuous four-step process:

1. Perceive: The system gathers information from its environment by incorporating data from APIs, databases, external sensors, and user-entered prompts. This is how it knows the goal it's trying to achieve.

2. Reason: An AI model, typically an LLM, takes the information the agentic AI system has gathered—including its goal and knowledge of available tools—to come up with a plan. This can require pulling in more data using processes like retrieval-augmented generation (RAG) or deploying other, more specialized AI models. 

3. Act: The system executes its plan by deploying AI agents and tools—typically through APIs or emerging agent protocols like Model Context Protocol (MCP) or Agent2Agent (A2A) protocol.

4. Learn: Successful strategies are reinforced by the AI system, while failed approaches lead the system to adjust its behavior over time, gradually improving performance.

Agentic AI examples

Agentic AI systems are already being deployed across enterprise organizations looking to scale complex, decision-heavy workflows without adding operational overhead. Here are a few examples of how teams are using Zapier Agents to build and run agentic AI workflows. 

• Sales follow-up and deal management: NisonCo used Zapier Agents to analyze sales call recordings, extract action items, draft personalized follow-up emails, and log all the relevant details in the CRM. From there, the agent learns to adjust its outreach strategy based on whether deals close.

• Hiring and candidate screening: At JBGoodwin Realtors, an agentic workflow reviews incoming applications and evaluates candidates against job criteria. Then, it verifies credentials through external sources, compiles candidate dossiers, and calculates a hireability score—refining hireability assessments based on who gets hired or not. 

• Lead generation and outreach: UK clean energy brand egg built an agentic system to scan inbound leads and external databases, enrich prospect data, and initiate outreach. From there, it analyzes response sentiment and routes qualified leads to sales systems while flagging negative feedback for review.

What is robotic process automation?

Robotic process automation (RPA) is a technology that uses virtual bots to execute repetitive, rules-based tasks that humans would otherwise perform. These bots mimic human interactions with software by doing things like clicking buttons, typing data, copying information, and moving files across applications. RPA can work with any graphical user interface (GUI) that human users can, whether it's a modern web app or a legacy system from a bygone era.

How does RPA work? 

Robotic process automation follows a predictable, three-part process:

1. Capture: The bot records the steps required to complete a task—such as logging into an application, navigating menus, or extracting data.

2. Process: The bot applies predefined rules and conditional logic to determine what actions to take.

3. Execute: The bot performs the task exactly as programmed. 

While RPA is one unified technology, it can be used in three different ways. 

• Attended RPA works alongside humans to complete tasks in real time. 

• Unattended RPA lets the bots run independently, using predefined rules and triggers, to complete tasks. 

• Hybrid RPA combines both approaches, splitting time between working independently and supplementing human users with key information. 

It's worth noting that RPA isn't the same as workflow automation. You can use RPA in conjunction with workflow automation and AI tools, but RPA is its own separate technological beast.

Examples of RPA

Here are three hypothetical (but completely possible) examples that showcase the potential of deploying RPA to process high-volume, repetitive tasks. 

• Finance: Bots extract data from invoices, enter it into ERP systems, and route it for payment approval without requiring API access.

• Manufacturing: Software monitors inventory levels in legacy systems, generates purchase orders, updates production schedules, and compiles quality control reports.

• Billing and revenue operations: Bots send payment reminders to users, lock accounts with missed payments, and generate billing reports.

Agentic AI vs. RPA: Similarities and differences 

At a high level, agentic AI and RPA are both designed to automate work that would otherwise be handled by humans. Agentic AI systems are well-suited to planning, reasoning, and deciding what should happen next, while RPA excels at executing clearly defined actions, particularly inside systems that don't offer APIs or modern integrations.

Agentic AI vs. RPA: Similarities 

Agentic AI and RPA share several foundational goals:

• Both automate operational work traditionally performed by people.

• Both aim to reduce human error and improve consistency.

• Both can run with minimal ongoing supervision.

• Both are widely used in enterprise environments.

Agentic AI vs. RPA: Differences 

Despite these similarities, the two technologies diverge sharply in how they operate and what they're best suited for.

Agentic AI

• Primary goal: Achieve outcomes and complete goals, not just individual steps. 

• Intelligence model: Reasoning-based, typically powered by LLMs and other AI models. 

• Adaptability: High—can adjust plans and strategies as conditions change.

• Handling unstructured data: Strong—can work with text, transcripts, documents, and other unstructured inputs.

• Learning from outcomes: Yes—can reinforce successful approaches and adjust after failures.

• Typical interfaces: APIs, tools, and coordinated AI agents.

• Best suited for: Complex, dynamic workflows that involve judgment and coordination.

Robotic process automation (RPA)

• Primary goal: Execute predefined tasks exactly as specified.

• Intelligence model: Rules-based, using explicit logic and conditions.

• Adaptability: Low—follows scripts and breaks when processes change.

• Handling unstructured data: Limited—works best with structured, predictable inputs.

• Learning from outcomes: No—repeats the same process every time.

• Typical interfaces: Graphical user interfaces (clicks, keystrokes, screen interactions).

• Best suited for: Stable, repetitive processes that require consistency and precision.

Agentic AI vs. RPA: Which should you use?

In isolation, RPA is ideal for predictable, high-volume tasks that don't change often and require absolute consistency—especially when you're working with legacy systems or software that doesn't offer APIs. Agentic AI is better suited for workflows that involve judgment, ambiguity, and evolving goals, where systems need to decide what to do next rather than follow a fixed script.

If the workflow follows a clearly defined set of steps, runs at high volume, and needs to be executed the same way every time—particularly inside systems without modern integrations—RPA is usually the better choice. If the work involves interpreting information, making decisions, or coordinating actions across multiple tools and teams, agentic AI is often a better fit.

That said, most teams don't need to go all-in on one approach. Increasingly, the most effective setups combine agentic AI, workflow automation, and (where necessary) RPA. Agentic AI can handle planning and decision-making, while automation executes actions across your tech stack—and in many cases, modern integrations mean you don't need an army of bots clicking through screens at all.

That's where Zapier comes in. Zapier helps you start automating right away—whether you're building agentic workflows with Zapier Agents or creating deterministic automations that connect thousands of apps. You can experiment, scale gradually, and add intelligence where it actually delivers value, without handing full control of your systems to AI before you're ready.

Related reading: 

• How Slate generated 2000+ leads with Zapier Agents

• What is AI agent orchestration? 

• AI agent use cases: How real teams are using AI agents at work 

• What is Claude Cowork? The powerful AI assistant for non-developers