Modern testing is still stuck behind one stubborn problem: test data.

We spend hours creating it, maintaining it, and coordinating across teams—yet still struggle to test the scenarios that matter most: failures, edge cases, inconsistent responses, and real user journeys across distributed systems.

In API-driven architectures, this problem is amplified. A single user flow depends on multiple services, each with its own failure modes, latency, and evolving contracts. Reproducing these conditions reliably is difficult, and even when we do, validation often breaks down—leaving gaps between what we test and what actually happens in production.

What if we could test real scenarios on real systems—and validate them visually with high confidence—without writing more code?

In this session, we’ll go beyond traditional approaches and show how to combine live traffic simulation using Specmatic Live Proxy with AI-powered visual validation using Applitools Visual AI. This enables teams to simulate real-world conditions—like API failures, latency, and unexpected data—and validate UI outcomes deterministically across these scenarios.

Rather than showcasing multiple disconnected capabilities, we’ll focus on a single critical user flow and evolve it across three targeted scenarios:

- Failure scenario → demonstrates controlled backend condition simulation
- Edge case (empty/inconsistent response) → exposes UI regressions beyond status codes
- Latency scenario → validates resilience of UI behavior and visual checkpoints

We’ll also make explicit what changes in the test configuration between scenarios—so the approach is clear, repeatable, and not “magic.”

You’ll see how this approach:

- Eliminates dependency on complex test data
- Expands coverage to include failure and edge scenarios
- Reduces brittle assertions and test code
- Improves confidence with consistent, AI-driven validation

Through a live demo on a deployed system, we’ll simulate real-world conditions and validate UI behavior across scenarios—without changing backend systems or duplicating test logic.

If your goal is to test what truly happens in production—and validate it with confidence—this session will give you a practical, scalable way forward.

Learning outcome:
- Define clear simulation boundaries by deciding what to intercept vs what must remain real in API-driven flows
- Stabilize UI validation in dynamic applications using visual AI—reducing flakiness and brittle assertions
- Increase scenario coverage without test duplication by designing a single reusable test flow across multiple backend conditions
- Differentiate real regressions from noise by tuning visual validation for meaningful, user-visible changes
- Combine backend simulation with validation effectively to test production-like scenarios with higher confidence