How is Agentic AI different from RPA?

Current Scenario: Understanding RPA and Agentic AI

Robotic Process Automation (RPA) has been a staple in business automation for over a decade. It involves software robots or “bots” that mimic human actions to execute rule-based, repetitive tasks across applications. RPA excels at structured processes with clearly defined inputs and outputs, following predetermined paths and rules without deviation.

Agentic AI, on the other hand, represents a more recent evolution in automation technology and form of autonomous process automation. These systems can autonomously perceive their environment, reason through complex problems, make decisions, and take actions to achieve specific goals.

Unlike RPA, agentic AI can learn from interactions, adapt to changing conditions, and handle ambiguity—moving beyond simple task execution to problem-solving, making them a viable RPA alternative for complex decision-making.

Industry Applications and Examples of RPA and Agentic AI

Banking and Financial Services

RPA Implementation: Banks deploy RPA for transaction processing, account management, and compliance reporting.

For example, Banks with RPA bots can handle millions of daily transactions, automatically reconcile accounts and flag anomalies based on predefined rules.

RPA Workflow in Banking:

1. The RPA bot logs into the banking system using secure credentials
2. It extracts transaction data from multiple sources (payment systems, account databases)
3. The bot applies predefined validation rules to check for discrepancies
4. It matches transactions against expected patterns
5. For exceptions, the bot flags items for human review in a predefined queue
6. The bot generates standardized reports according to regulatory requirements
7. Process terminates until the next scheduled run

Agentic AI Application: Financial service companies can employ agentic AI for fraud detection that continuously learns from new patterns. When suspicious activity occurs, the AI system autonomously decides whether to block transactions, request additional verification, or alert human investigators based on the specific situation’s context and severity.

Agentic AI Workflow in Banking:

1. The AI continuously monitors transaction streams in real-time
2. It analyzes each transaction against learned patterns of normal customer behavior
3. When detecting an anomaly, it evaluates multiple contextual factors (location, amount, merchant type, transaction history)
4. Based on risk assessment, the AI autonomously decides the appropriate action:
   1. Allow the transaction if risk is low
   2. Request additional verification (e.g., send text verification) if risk is moderate
   3. Block the transaction and alert the fraud team if risk is high
5. The system learns from each decision outcome to improve future assessments
6. It periodically reassesses its own decision patterns and adjusts detection parameters

Comparison: In the RPA vs Agentic AI discussion, Agentic AI offers significant advantages in real-time risk assessment and autonomous actions.

Insurance

RPA Implementation: Insurance companies use RPA for claims processing and policy administration. Insurance companies can deploy bots that extract data from claim forms, validate coverage, and route claims to appropriate departments based on strictly defined workflows.

RPA Workflow in Insurance:

1. The RPA bot monitors the claims inbox for new submissions
2. When a new claim arrives, it extracts structured data from digital forms
3. The bot verifies policy details in the customer database
4. It validates coverage based on predefined policy rules
5. The bot calculates preliminary payment amounts using fixed formulas
6. It routes the claim to the appropriate department based on claim type and value
7. The bot sends automated acknowledgment to the customer
8. Process ends until the next claim arrives

Agentic AI Application: Insurance companies utilizes agentic AI for damage assessment after accidents. Customers upload photos of vehicle damage through an app, and the AI agent evaluates the severity, estimates repair costs, suggests nearby repair shops and, in some cases, approves immediate payment—all without human intervention. This shift illustrates a real-world case of autonomous process automation replacing static RPA workflows.

Agentic AI Workflow in Insurance:

1. Customer uploads accident photos and report through mobile app
2. The AI analyzes images using computer vision to identify damage patterns
3. It compares damages against a database of similar cases and repair costs
4. The AI evaluates the claim context (policy details, accident description, driver history)
5. Based on its analysis, it dynamically:
   1. Generates a repair cost estimate
   2. Identifies suitable repair shops based on location, expertise, and capacity
   3. Determines if immediate payment approval is warranted
6. For complex cases, it identifies specific questions to clarify with the customer
7. The AI learns from adjustments made by human reviewers to improve future estimates
8. It follows up with customers based on their response patterns and preferences

Human Resources

RPA Implementation: HR departments implement RPA for employee onboarding and payroll processing. A typical RPA solution in HR might automatically create email accounts, assign access rights, and update HR databases when a new employee joins.

RPA Workflow in HR:

1. HR staff inputs new hire information into a standardized form
2. The RPA bot extracts this data and triggers the onboarding workflow
3. It creates accounts in multiple systems (email, payroll, benefits)
4. The bot assigns access rights based on predefined role templates
5. It generates standard onboarding documents using templates
6. The bot sends notification emails to relevant departments
7. It schedules required orientation meetings in the calendar system
8. Process completes until the next new hire

Agentic AI Application: HR department uses agentic AI for talent acquisition and development. The AI agent analyzes job markets, identifies skill gaps within the organization, proactively reaches out to qualified candidates, conducts initial screening interviews, and recommends personalized development plans for existing employees. The flexibility of Agentic AI in HR again positions it as a strong RPA alternative in talent management.

Agentic AI Workflow in HR:

1. The AI continuously analyzes internal skills data and external job market trends
2. It identifies emerging skill gaps and prioritizes hiring needs
3. The system proactively searches for candidates across multiple platforms
4. It personalizes outreach based on candidate profiles and communication preferences
5. The AI conducts initial screening through conversational interfaces
6. It evaluates candidates using multiple criteria (skills, experience, cultural fit)
7. For existing employees, it creates personalized development recommendations
8. The system adapts its hiring and development strategies based on business outcomes
9. It autonomously adjusts its assessment criteria based on successful hires’ performance

Information Technology

RPA Implementation: IT teams employ RPA for system monitoring and basic troubleshooting. RPA bots can monitor network performance, restart failed services, and generate reports according to fixed schedules and parameters.

RPA Workflow in IT:

1. RPA bots run scheduled checks on system performance metrics
2. They compare results against predefined thresholds
3. If values exceed thresholds, bots execute predefined remediation scripts
4. For common errors, bots restart services according to fixed protocols
5. They log all actions in the ticketing system
6. Bots generate standardized reports on system status
7. Process repeats according to monitoring schedule

Agentic AI Application: IT operations can utilize agentic AI for proactive system management. The AI identifies potential issues before they impact operations, automatically reallocates resources during usage spikes, negotiates with various system components to optimize performance, and implements security patches with minimal disruption. This leap in capability is a key differentiator in the RPA vs Agentic AI landscape.

Agentic AI Workflow in IT:

1. The AI continuously analyzes patterns across the entire IT infrastructure
2. It predicts potential failures based on subtle pattern changes before alerts occur
3. The system proactively reallocates computing resources based on usage predictions
4. It negotiates with different system components to optimize overall performance
5. For security updates, it determines optimal deployment timing based on user activity
6. The AI simulates patch impacts before implementation
7. It learns from each intervention to improve future predictions
8. The system autonomously adjusts its monitoring focus based on emerging patterns
9. It communicates with users in natural language about potential issues and solutions

Home Care Services

RPA Implementation: Home care providers use RPA for appointment scheduling and billing. Bots collect patient information through forms, check insurance eligibility through predefined API calls, and generate billing codes based on documented services.

RPA Workflow in Home Care:

1. Patient data is entered into intake forms
2. RPA bot extracts data and creates patient record in the system
3. The bot verifies insurance coverage through standardized API calls
4. It checks caregiver availability in the scheduling database
5. The bot assigns caregivers based on predefined matching rules
6. After service delivery, it processes documentation using fixed templates
7. The bot generates billing codes according to documented services
8. It submits claims through standard electronic interfaces
9. Process concludes until the next patient intake

Agentic AI Application: Homecare companies deploy agentic AI companions for seniors that monitor medication adherence, vital signs, and daily activities. These AI agents adapt communication styles based on patient preferences, make independent decisions about when to alert healthcare providers, and adjust care recommendations based on observed patterns and outcomes. This is another space where smart automation outperforms rule-based bots.

Agentic AI Workflow in Home Care:

1. The AI companion continuously monitors patient activities and vital signs
2. It learns individual patterns of behavior and medication routines
3. The system adapts its communication style based on patient responses
4. When detecting deviations (missed medication, unusual activity patterns), it:
   1. Evaluates the significance based on patient history
   2. Decides appropriate intervention level (reminder, family notification, emergency alert)
5. It autonomously adjusts care recommendations based on observed outcomes
6. The AI coordinates with multiple caregivers and family members
7. It provides personalized health education based on patient condition
8. The system learns from successful interventions to improve future recommendations
9. It continuously reassesses patient status and adapts care strategies accordingly

Key Differences Between Agentic AI and RPA

The Future: Integration Rather Than Competition

As businesses mature in their automation journey, many are discovering that RPA and agentic AI are complementary rather than competing technologies. RPA excels at structured, repetitive tasks with clear rules, while agentic AI shines in complex, dynamic environments requiring judgment and adaptation.

Forward-thinking organizations are implementing hybrid approaches. For example, a mortgage processing system might use RPA bots to gather and validate application data, while agentic AI evaluates the overall risk profile, negotiates loan terms, and makes approval decisions based on broader market conditions and organizational goals.

This integrated approach leverages the reliability and efficiency of RPA for routine operations while harnessing the adaptability and intelligence of agentic AI for higher-value decision-making and customer interactions.

As both technologies continue to evolve, the distinction between them may blur further. RPA platforms are increasingly incorporating AI capabilities, while agentic AI systems are becoming more accessible and easier to implement.

What remains clear is that successful automation strategies will require thoughtful application of both technologies based on specific business needs rather than a one-size-fits-all approach.

As automation continues to evolve, organizations must evaluate the limitations of RPA and consider Agentic AI to stay competitive.

Frequently Asked Questions