The push to automate repetitive work has shaped every major transformation in how we work. From machines taking over physical labor during the industrial revolution, to computers handling calculations and record-keeping, each step has freed people to focus on higher-value tasks.
In recent decades, RPA showed how software could take over repetitive office tasks at scale. Now, a new paradigm is emerging: Agentic AI, systems that act toward goals with autonomy and adaptability.
Both share the aim of reducing repetitive work and improving efficiency, but they achieve it in fundamentally different ways.
What is RPA?
Robotic Process Automation (RPA) uses software bots to mimic human actions on a computer, following predefined scripts to complete tasks.
How it works
- Process mapping: Every click, keystroke, and rule is programmed in advance by IT teams.
- Execution: Bots replicate these steps across applications with speed and accuracy.
- Scope: Best suited for structured, repetitive workflows such as data entry, form filling, and transferring information between systems.
- Maintenance: Any change in process or interface requires updates to the scripts.
Strengths
- Reliable for repetitive, structured tasks.
- Reduces human error in routine processes.
- Widely adopted in enterprise operations like finance, HR, and back-office functions.
Limitations
- Easily breaks when processes or systems change.
- Unable to process unstructured or ambiguous data.
- Dependent on IT teams for setup and maintenance.
- Limited scalability for complex or evolving workflows.
What is Agentic AI?
Agentic AI refers to systems that autonomously plan, decide, and act to achieve goals without needing step-by-step instructions from humans.
How it works
- Goal intake: Understands objectives expressed in plain language.
- Reasoning: Uses AI, often powered by large language models, to plan steps and choose actions.
- Tool & data access: Connects with APIs, software, and databases to collect information and take action.
- Orchestration: Coordinates tasks in sequence, manages dependencies, and adapts when conditions change.
Strengths
- Handles complete workflows from start to finish.
- Adjusts dynamically when processes shift or data changes.
- Scales to support complex, multi-step operations.
- Frees teams from repetitive manual work.
Limitations
- Errors in one step can cascade through the process.
- Needs clear guardrails and human oversight to operate safely.
- Requires transparency so organizations can track and understand decisions.
RPA vs Agentic AI
The two approaches reflect different philosophies of automation: one focused on strict execution, the other on adaptive goal achievement.
| Aspect |
RPA |
Agentic AI |
| Primary role |
Executes predefined tasks |
Achieves goals autonomously |
| Nature |
Scripted, rule-based |
Reasoning-driven, adaptive |
| Input needed |
Step-by-step instructions |
Clear goal or outcome |
| Flexibility |
Brittle to change |
Adjusts dynamically |
| Data |
Structured only |
Structured and unstructured |
| Maintenance |
IT-heavy |
Self-adjusting with guardrails |
| Business value |
Efficiency in stable processes |
Efficiency and scalability in complex processes |
Practical examples
The differences between the two approaches become clearer when applied to common business workflows.
Finance
- RPA: Uses predefined templates to copy invoice data into an ERP system, following exact steps set up in advance.
- Agentic AI: Interprets invoices in varied formats, checks for inconsistencies, updates the ERP, and can reach out to vendors if information is missing.
Customer Support
- RPA: Routes tickets according to fixed rules, such as keywords or categories mapped ahead of time.
- Agentic AI: Reads entire customer messages, identifies intent, responds to straightforward requests, and passes along more complex cases for review.
Operations
- RPA: Syncs information between two systems on a predefined schedule, executing scripted actions step by step.
- Agentic AI: Observes system data continuously, identifies discrepancies, updates records, and notifies stakeholders when unusual patterns appear.
Adressing the challenges
RPA delivers value for stable, rule-based processes, while Agentic AI opens the door to automating more complex and adaptive workflows. The change marks a move from following predefined scripts to achieving outcomes with autonomy.
Success with Agentic AI depends on addressing challenges such as reliability, oversight, and transparency. At Maisa, we are building Digital Workers with accountability at their core. They are designed to reason clearly, act consistently, and explain their decisions so businesses can scale automation with confidence.
