AgentisIQ - RPA to AI Agent Conversion Demo

Step 1: Existing RPA Workflow

Let's look at a typical RPA script for basic P&C claim validation. It relies heavily on specific UI elements (like button IDs) and fixed steps, making it brittle.

Example: RPA Claim Validation Script


# RPA Script: Validate Claim Data Entry
LOG "Starting claim validation..."

# Find and click specific fields/buttons
CLICK element_id='policy_number_field'
TYPE text='{policy_num}'

CLICK element_id='claim_amount_field'
TYPE text='{claim_amt}'

# Check for specific confirmation text
CLICK element_id='validate_button'
WAIT 3 seconds # Fixed wait time

READ text from element_id='status_message' into status_var

# Logic based on exact text match
IF status_var == "Validation Successful" THEN
  LOG "Claim data validated."
ELSE IF status_var == "Error: Invalid Policy #" THEN
  LOG "ERROR: Policy number invalid."
  STOP # Hard stop
ELSE
  LOG "ERROR: Unknown validation status."
  STOP # Hard stop
ENDIF

LOG "RPA script finished."

Step 2: AgentisIQ Conversion

AgentisIQ analyzes the RPA script's logic and intent. Using the Model Context Protocol (MCP) tailored for P&C insurance, it understands the underlying business process, not just the UI clicks.

 Analyzing RPA workflow and applying MCP...

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This process transforms the fragile script into a robust, goal-oriented AI Agent.

Step 3: Resulting AI Agent

The conversion results in an AI Agent definition. Notice how it focuses on the goal (Validate Claim Data) and uses contextual understanding (Claim Schema via MCP) rather than fixed UI elements.

Example: AgentisIQ AI Agent Definition


# AgentisIQ Agent: Validate_Claim_Data
AGENT_NAME: P&C Claim Validator Agent
GOAL: Validate core claim data (policy #, amount) against system rules.

CONTEXT:
  - Uses P&C Claim Schema (via MCP)
  - Understands standard validation outcomes (Success, Invalid Policy, Invalid Amount, System Error)

TRIGGERS:
  - New Claim Data Received

STEPS:
  1. IDENTIFY policy_number, claim_amount from input_data.
  2. VALIDATE policy_number format (using Claim Schema).
  3. QUERY policy_system API (is_policy_active(policy_number)).
     - HANDLE API errors gracefully.
  4. VALIDATE claim_amount range/rules (using Claim Schema).
  5. DETERMINE overall validation_status based on steps 2-4.
  6. OUTPUT validation_status and details (e.g., specific error type).

RESILIENCE:
  - Adapts to API changes (if contract remains compatible).
  - Tolerates minor UI variations in source systems.
  - Provides clearer error reporting based on validation logic.

This agent is more resilient to system changes, easier to maintain, and provides better error handling.

Step 4: Key Advantages

Converting RPA to AgentisIQ AI Agents delivers significant improvements:


Increased Resilience: Agents adapt to UI/API changes that would break traditional RPA scripts.

Reduced Maintenance: Less time spent fixing broken bots, more time on valuable automation.

Faster Deployment: Define agents based on business goals, not tedious scripting. MCP accelerates understanding.

Improved Error Handling: Context-aware agents provide clearer diagnostics when issues arise.

Scalability: Easier to manage and scale complex automation across the P&C value chain.

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