The Challenge of Testing AI-Driven Platforms
Modern businesses rely heavily on data, but collecting and analyzing it at scale isn’t simple. When Agiliway began building an AI-powered platform designed to discover potential business leads by scraping websites, social networks, and industry databases, a significant obstacle emerged: quality assurance.
Traditional QA approaches – where code is built first and only later tested – proved too slow and fragile. The rapid pace of AI development meant bugs were surfacing late, fixes were costly, and testing often failed to reflect the system’s real-world complexity.
Why Old Testing Models Weren’t Enough
AI-based platforms don’t behave like conventional software. They evolve constantly, consume data from unpredictable sources, and require legal compliance around every corner. Manual testing alone couldn’t keep up.
Relying on after-the-fact test cycles meant that by the time issues were spotted, developers had already moved ahead. In practice, this slowed progress, frustrated teams, and risked releasing unstable features.
Merging Development and Testing
To address the bottleneck, Agiliway implemented a new model: continuous QA automation fully integrated into the development cycle.
Instead of waiting until features were finished, developers used Windsurf IDE with an AI assistant that generated test cases in parallel with the code itself. Quality checks instantly accompanied each new line of logic. Bugs surfaced early, often within minutes of being introduced, and were fixed before they became deeply embedded in the system.
This wasn’t just a productivity boost – it changed the culture. Testing became a natural part of building, not a chore that lagged behind.
Smarter Browser and UI Testing
User interfaces present another notorious pain point. Filtering, dashboards, and real-time updates are especially hard to validate at scale. For this reason, the team chose Playwright, which comes with its own browser and avoids the flaky setup common with older tools.
Playwright’s integration with Windsurf’s MCP server enabled new interface features – such as “Magic Mode” for auto-filling filters – to generate their own automated tests without manual scripting.
In effect, the platform tested itself as it grew.
Tests with Real Business Context
Automation only adds value if tests remain relevant. To ensure this, the framework is connected with Jira and Confluence via the Atlassian MCP server. It pulled requirements, combined them with the actual code, and generated tests that checked not only technical accuracy but also business outcomes.
For example, instead of just verifying “the filter works,” tests asked: Can a user identify companies that fit their ideal customer profile and rank them correctly?
This contextual approach ensured that tests aged gracefully with changing requirements, avoiding the trap of becoming outdated scripts nobody trusted.
One Source of Truth for QA
Another common problem in large projects is test duplication across different tools. To avoid this, the team synchronized everything with TestRail. Automated cases were stored alongside manual ones, giving stakeholders a single view of coverage.
This allowed project managers to confirm whether business requirements were being met, while developers knew their automated tests complemented human validation instead of clashing with it.
Faster Bug Resolution
When a test failed, the system didn’t just raise a red flag. It automatically created a detailed Jira ticket, attached screenshots, and included reproduction steps. Bugs were also routed to the right person instantly.
This eliminated the common back-and-forth between QA and development. Instead of vague “something broke” messages, developers received actionable tasks they could resolve immediately.
Tangible Results
The impact was immediate:
Earlier bug detection prevented issues from reaching users.
Manual testing effort dropped significantly.
Resolution times shortened, thanks to automated, detailed bug reports.
Tests adapted continuously, evolving as the platform grew and requirements shifted.
Instead of QA slowing down development, it became the accelerator.
Leave a Reply