The three paths from RPA to agentic AI

A flexible, composable framework to balance platform fit and business ambition

Our transformation methodology offers three distinct, composable pathways:

Augment (Capability Shift): Reimagine at the task level by replacing rule-based steps with agentic reasoning, computer use agents, and document processing while preserving core operating models.
Reimagine (Process Shift): Redesign entire workflows around goals, outcomes, and new KPIs using multi-agent orchestration and adaptive learning loops.
Replatform (Platform Shift): Migrate off legacy RPA to agentic-native target platforms using MigrationPro, preserving business logic while transforming the execution substrate.

To determine candidate processes, Virtusa applies a 5-tier strategy.

The 5-tier agentic execution and migration engine

Tier	Description
Tier 1: Inventory mining and bot classification	Code and log mining: Extract legacy XAML files, screen-scraping selectors, and runtime logs from current platforms. 5-Tier taxonomy audit: Classify automations to isolate unstable visual interfaces from stable system endpoints. Financial baseline: Map execution volume against token cost models to determine exact migration pathways.
Tier 2: Logic decoupling and data structuring	API transition: Replace rigid user interface scripts with native backend integrations for Tier 1 processes. Unstructured data pipeline: Convert dynamic documents and free-form reports into typed schema objects using generative models. Rule extraction: Isolate embedded business logic from legacy bots and encapsulate it in modular, reusable decision prompts.
Tier 3: Multi-agent perception and action layer	Vision and DOM perception: Analyze DOM elements, HTML tags, and visual interface heuristics without brittle selectors. LLM decision engine: Process variable context, weigh operational parameters, and determine optimal task sequences. Action executor: Execute physical system actions, keyboard inputs, and application calls across target environments.
Tier 4: Governance and token management	Semantic regression testing: Run automated evaluation suites to verify deterministic outputs and prevent model hallucinations. Cost control gateway: Deploy centralized prompt caching and semantic routing to optimize cloud execution spend. Human-in-the-loop intercepts: Route ambiguous edge cases and policy exceptions to human operators through approval queues.
Tier 5: Telemetry and closed-loop learning	Observational tracing: Record end to end reasoning paths, prompt inputs, and system actions for compliance auditing. Autonomous self-correction: Monitor execution failures in real time and update spatial coordinates dynamically. Outcome telemetry: Track cycle time reductions, manual effort savings, and overall process accuracy.

50–70%

60%

40–60%

3–5x

100%

24/7

3+

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