Submission type
UiPath for Coding Agents
Name
Ranveer Singh Thakur
Industry category in which use case would best fit in (Select up to 2 industries)
Insurance
Complexity level
Advanced
Summary (abstract)
AIInvoiceProcessor is an enterprise-style UiPath automation project built using AI coding agents, UiPath CLI, and REFramework principles. The project demonstrates how coding agents can assist across the full automation lifecycle — from architecture and workflow generation to debugging, packaging, and deployment preparation.
The solution processes PDF invoices using queue-based transaction handling, Config.xlsx-driven settings, retry and exception strategies, and structured logging. The project also explores multi-agent collaboration, human-in-the-loop approvals, and AI-assisted recovery from automation failures, showcasing a modern approach to AI-powered RPA development.
Detailed problem statement
The goal of this project was to explore how AI coding agents can move beyond simple code generation and actively participate in the complete UiPath automation development lifecycle.
Traditional RPA development still requires significant manual effort for project setup, framework design, exception handling, selector management, deployment preparation, and debugging. This project investigates whether coding agents can assist like enterprise RPA developers by helping design, scaffold, troubleshoot, and operationalize a production-style UiPath automation solution.
Using Claude Code along with the UiPath CLI and skills ecosystem, the project focused on building an enterprise-grade invoice processing automation using REFramework principles. The coding agents were used to:
- Generate project architecture and folder structures
- Recommend REFramework implementation patterns
- Design queue-based transaction processing
- Create resilient retry and exception handling strategies
- Suggest strict selector and Object Repository usage
- Assist with deployment and packaging workflows
- Troubleshoot CLI and automation issues during execution
- Simulate multi-agent collaboration across architecture, development, testing, and operations
The project demonstrates how coding agents can act as collaborative automation partners capable of reasoning across multiple stages of the automation lifecycle rather than functioning only as autocomplete or snippet-generation tools.
Detailed solution
Detailed Solution
For this challenge, I built AIInvoiceProcessor, an enterprise-style UiPath automation project powered by AI coding agents, UiPath CLI, and REFramework principles. The solution demonstrates how coding agents can assist across the full automation lifecycle instead of only generating snippets.
What Got Built
The project processes PDF invoices using:
- Queue-based transaction handling
- REFramework architecture
- Config.xlsx-driven settings
- Retry and exception handling
- Structured logging
- Failure recovery workflows
- Packaging and deployment preparation through UiPath CLI
The automation was scaffolded and structured using AI coding agents running from a macOS terminal environment.
Coding Agents Used
Claude Code
Used as the primary architecture and orchestration agent.
Responsibilities:
- Planned REFramework structure
- Generated enterprise folder layout
- Suggested workflow breakdowns
- Designed retry and exception strategies
- Helped troubleshoot CLI and packaging issues
- Guided deployment flow and monitoring concepts
Codex
Used as the implementation-focused development agent.
Responsibilities:
- Suggested reusable workflow patterns
- Assisted with utility workflow generation
- Helped create logging and screenshot handling strategies
Gemini CLI
Used as the testing and validation agent.
Responsibilities:
- Reviewed resiliency patterns
- Validated retry logic
- Analyzed selector and exception risks
- Simulated QA review of workflow stability
Customization and Prompt Engineering
I customized the coding-agent workflow using dedicated system prompts for different responsibilities.
Examples included:
- Architect prompt for enterprise UiPath standards
- Builder prompt for modular workflow implementation
- Tester prompt for resiliency and validation
- Operations prompt for deployment and monitoring flows
These prompts helped the agents reason more like specialized enterprise RPA team members rather than general-purpose assistants.
The prompts enforced:
- REFramework usage
- Config.xlsx-driven design
- Strict selectors
- RetryScope implementation
- Queue-based processing
- Reusable components
- Logging and screenshot capture
- Human-in-the-loop deployment thinking
Multi-Step Reasoning
One of the most important aspects of the project was maintaining lifecycle continuity across:
Build → Run → Debug → Fix → Package → Deployment Preparation → Monitoring
The coding agents retained context while troubleshooting:
- CLI command mismatches
- Packaging syntax issues
- Project structure validation
- Recovery strategies for failed automation steps
This demonstrated how AI agents can collaborate across multiple stages of automation delivery instead of operating in isolated prompts.
Multi-Agent Collaboration
The project explored a multi-agent setup where:
- Claude Code acted as the architect and coordinator
- Codex acted as the implementation assistant
- Gemini CLI acted as the testing and resiliency reviewer
This created a workflow similar to a real enterprise automation team with distinct responsibilities across architecture, development, QA, and operations.
Human-in-the-Loop Design
The project intentionally included human approval checkpoints before risky operations such as:
- Packaging
- Publishing
- Deployment to Orchestrator environments
This was done to simulate enterprise governance and safe deployment practices for AI-assisted automation systems.
UiPath Skills and CLI Usage
The project used the new UiPath CLI and skills ecosystem to:
- Install and manage skills
- Scaffold automation workflows
- Prepare projects for packaging
- Explore deployment and orchestration operations
- Integrate AI coding agents with UiPath tooling
The workflow highlighted how UiPath’s newer CLI ecosystem supports AI-native automation development patterns.
Demo and Failure Recovery
The demo focused on:
- AI-generated enterprise project scaffolding
- CLI-based automation lifecycle operations
- Multi-agent reasoning
- Real troubleshooting during packaging and validation attempts
- Recovery from command and deployment-related failures
Instead of hiding failures, the project intentionally showcased debugging and recovery flows to demonstrate real-world AI-assisted automation engineering.
Key Takeaway
What surprised me most was how quickly the coding agents started behaving more like collaborative automation teammates than traditional coding assistants. They were able to reason across architecture, debugging, resiliency, and deployment workflows while maintaining enterprise automation patterns and lifecycle context.
Narrated video link (sample: https://bit.ly/4pvuNEL)
I have created Walkthrough Document of all the things I have tried
Submission.Docx in GitHub (it also has complete project)
Expected impact of this automation
Expected Impact of the Automation
The expected impact of this project is to demonstrate how AI coding agents can significantly accelerate and modernize enterprise RPA development workflows.
By combining UiPath automation with AI coding agents, the solution reduces the manual effort traditionally required for:
- Project scaffolding
- Framework setup
- Exception handling
- Retry strategy implementation
- Deployment preparation
- Debugging and recovery workflows
The automation showcases how coding agents can help developers move faster while still following enterprise-grade practices such as REFramework, Config-driven development, strict selectors, queue-based processing, and structured logging.
From a business perspective, the solution can help:
- Reduce development and debugging time
- Improve automation reliability and resiliency
- Standardize enterprise automation patterns
- Speed up onboarding for new developers
- Enable faster automation delivery cycles
From a technology perspective, the project highlights the future direction of AI-assisted automation engineering, where coding agents act as collaborative development partners across architecture, implementation, testing, deployment, and operational monitoring.
UiPath products used (select up to 4 items)
UiPath Studio
Automation Applications
PDF, Excel
Integration with external technologies
Claude Code
