RPA bots and AI agents live in messy business systems. Production data is often private or incomplete, and creating realistic test scenarios means reusing real user sessions. That approach is risky and expensive. Synthetic data solves those problems by generating lifelike workflows on demand, letting teams test automation against diverse, safe inputs.
Why regression tests fail in real RPA setups
RPA bots touch thousands of fields across legacy systems, web portals, and custom apps. When a business rule changes or a new field appears, bots break. Teams run regression tests to catch regressions, but they usually rely on a handful of existing user sessions. Those sessions may not cover new workflows, edge cases, or rare data combinations. A bot that worked last month might fail next week on a slightly different record, and teams might not discover the problem until production does.
The cost of real-world test data
Real user workflows are hard to capture at scale. You need consent, anonymization, and careful logging. Even when you have logs, they often miss transient states like popups, file downloads, or multi-step approvals. A 2023 study of enterprise automation teams found that 40 percent of test failures were due to missing or outdated test scenarios. Rebuilding those scenarios manually costs hours per week. Teams end up with shallow test coverage and slow feedback loops.
How synthetic data improves RPA regression testing
Synthetic data generation creates new, realistic workflows without touching production. You define the business context, departments, roles, workflows, and the system generates varied input sets that follow the same rules as real users. For example, if an order approval workflow requires a manager signature, synthetic data can produce many orders with different statuses, amounts, and approval paths. This lets you test edge cases like partial approvals, rejected workflows, or missing approvals. The key is realism: the generated sequences should feel like they came from a human, not random noise.
Practical techniques for synthetic regression testing
- Use domain models to capture business rules, like product hierarchies, approval chains, and pricing formulas.
- Generate sequences that mimic real workflows: login, browse, enter data, submit, and handle outcomes.
- Inject noise, such as occasional typos, missing fields, or unexpected UI states, to stress-test error handling.
- Batch-create scenarios for different departments, regions, or time periods to increase coverage.
- Integrate synthetic data into CI/CD pipelines so regression tests run automatically after code changes.
Synthetic data gives teams a safe, scalable way to cover edge cases and rare workflows, reducing regression failures and speeding up deployment.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. It captures realistic interaction data and can produce custom synthetic datasets and trajectories for training and evaluating agents. This means the synthetic workflows it generates match how humans actually move through applications. Coasty’s service is custom and contact-led: you discuss your workflows and objectives, and the team builds datasets tailored to your automation stack.
If you want to build stronger regression tests for your RPA and automation projects, synthetic data is a practical starting point. Talk to the Coasty data team to explore how realistic, custom synthetic datasets can fit your workflow. Book a data call at https://cal.com/coasty/coasty-data-call.
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