We are a team of three developers working on the same RPA projects, and we keep overwriting each other's changes. It is a nightmare. Does everyone use Git for their RPA code, or are there internal tools? I am looking for a way to track changes and roll back if something breaks during deployment.
Implementing Git-based version control provides a robust framework for managing concurrent code contributions through branching and merge conflict resolution, which is essential for maintaining stability in team-based RPA development.
6 answers
The choice between vendor-specific internal tools and standard Git comes down to how much you value portability versus native integration. Proprietary tools like those found in some RPA suites are convenient if your team is entirely non-technical, but they often fail to offer robust merge conflict resolution when multiple people work on the same module simultaneously. Git is technically superior for distributed development workflows because it forces a deliberate merge process that prevents the accidental data loss you are currently experiencing.
You should absolutely be using Git regardless of the specific RPA platform you are currently deploying. Treat your bot logic as standard infrastructure-as-code to ensure your deployment pipelines have a definitive source of truth for every rollback event.
I recall dealing with a catastrophic merge conflict back in my early days when our automation scripts were treated as simple text files on a shared network drive. One developer's overwrite essentially wiped out a critical safety routine for an industrial controller, and we spent two days manually reconstructing the logic from old printouts.
We finally moved the entire repository to a versioned Git server, and it was a revelation. Having a clear audit trail of who changed what and when is not just a productivity booster, it is a non-negotiable security requirement when you are automating systems that interact with physical hardware.
To stop the file overwriting issues immediately, your team must shift to a centralized version control system using these specific operational protocols.
- Create a dedicated remote repository for your project code
- Require every developer to create a feature branch for their specific tasks
- Enforce mandatory pull request reviews before merging code into the main branch
- Implement a CI/CD pipeline that builds and tests the code before deployment
Stop using shared network drives or internal proprietary save features right now because they are structurally incapable of handling multi-user concurrency. Just move your code into Git and enforce a strict branch-based workflow. If you do not have a formal audit trail and a way to revert to a known good state, you are just waiting for a major production failure. It is not that complicated, so just get it done.
Managing version control in an RPA environment is fundamentally an engineering problem that requires moving away from visual drag-and-drop file management toward a code-centric paradigm. Even if your RPA platform uses a graphical interface, you need to extract the underlying configuration files into a format that Git can track effectively. By serializing your bot configurations as JSON or XML files, you gain the ability to perform line-by-line diffs which are impossible with binary project files. This visibility into incremental changes is what allows you to diagnose regressions during deployment without needing to restore a complete, dated backup of your entire automation workspace.
You should consider setting up a localized GitLab or GitHub runner that triggers a deployment test every time a push event occurs in your main branch. This creates a feedback loop where errors are caught in the development phase rather than in production. Furthermore, maintaining a history of commit messages provides the team with the context for why a specific architectural change was made, which is invaluable for long-term project maintenance. Transitioning to this approach might require a brief period of retraining for your team, but it will effectively eliminate the overwriting nightmare you described. Once you standardize your repository structure, the risk of breaking critical automation workflows is drastically reduced because you finally have the infrastructure to revert or cherry-pick specific changes with absolute confidence.
