From Scripts to Scenarios - How AI Understands What to Test

TL;DR

Traditional test scripts are too brittle for today’s fast-moving, complex systems. AI-powered agents enable a shift to scenario-based testing - high-level, reusable flows that describe user intent and behavior. Agents can help extract, generate, and evolve these scenarios, while humans guide relevance, risk, and validation. This approach improves stability, cross-platform coverage, and business alignment. Scenarios aren’t just a better way to test, they’re the foundation for intelligent agent orchestration in the future.

The future of test design isn’t scripted. It’s scenario-driven.

Traditional test automation relies on brittle, step-by-step scripts that fail as soon as the UI shifts or the data shape changes. As systems evolve faster and become more dynamic, so must our approach to defining what we test.

Enter the scenario model: a shift from hardcoded actions to semantic flows that describe intent, context, and expected outcomes. And with AI agents in the loop, we can now generate, prioritize, and evolve these scenarios with more speed and alignment than ever before.

Why Scripts No Longer Scale

Test scripts were built for a different era where:

• Interfaces were stable
• Business rules were hardcoded
• Release cycles were quarterly

But now:

• UIs change weekly
• Logic lives in APIs and models
• User journeys are fragmented across channels
• Test data is dynamic and sensitive

Hardcoded scripts can’t keep up. They’re:

• Time-consuming to write
• Fragile to maintain
• Blind to business context

Every time your app changes, your tests break  even if the user journey didn’t.

What Are Scenarios?

Scenarios describe intent not just steps.

For example:

“A returning customer logs in, adds an item to the cart, and checks out using a saved payment method.”

That’s a scenario. It can be:

• Expressed in natural language or Gherkin
• Decomposed into reusable components
• Replayed across platforms (web, mobile, API)
• Prioritized based on usage, risk, or change history

It’s not just a test. It’s a model of behavior.

How Agents Help With Scenarios

With the rise of LLMs and pattern-aware agents, we can now use AI to:

• Extract scenarios from user stories, acceptance criteria, or call logs
• Cluster similar flows into reusable scenario templates
• Generate test cases from those templates, across channels
• Highlight gaps between current coverage and real-world usage
• Evolve scenarios as the system changes

Agents don’t invent test logic from scratch. They surface what your system is already doing and where it might fail.

Example: From User Story to Test Scenario

Let’s say your story reads:

“As a customer, I want to reset my password so I can access my account.”

An agentic flow might look like this:

1. Parse the story and extract intent: password recovery
2. Reference prior examples of similar flows (login, MFA)
3. Draft a high-level scenario:
   • "User initiates password reset, receives email, resets password, and logs in"
4. Suggest variants:
   • Email not received
   • Token expired
   • Password strength failure
5. Output scenario cards for human review and test generation

The human test lead approves or adjusts these adding business rules or regulatory considerations (e.g., MFA lockout logic for financial apps).

Human-in-the-Loop Still Matters

AI can generate candidate scenarios but it doesn’t understand what matters to your business.

That’s where testers stay essential:

• Approving flows aligned to risk
• Rejecting noise or irrelevant suggestions
• Adding edge cases based on domain expertise
• Tagging scenarios by product, customer type, or feature group

Scenario design becomes a collaborative canvas, not a maintenance burden.

The Benefits of Scenario-First Testing

A Foundation for Orchestration

This scenario model also sets the stage for what’s coming next:

• Agents that collaborate to prioritize risk-based scenarios
• A Test Architect Agent that understands which flows to emphasize
• A Design Agent that refactors scenarios into modular test steps
• And a Summary Agent that reports on scenario coverage, not just pass/fail logs
  
  

Without scenarios, this orchestration becomes chaotic.
With scenarios, it becomes intent-driven and explainable.

From Scripts → Scenarios → Strategy

This is the inflection point.

• Scripts helped us automate.
• Scenarios help us think.
• Agents help us scale.
  

By embracing scenario-based testing, you enable AI to become a real partner in design — not just in execution.

The shift isn’t just from manual to automated.
It’s from reactive to strategic.

Coming Up Next:

Blog 6: Designing Your Virtual Test Team
We’ll introduce the core agent roles that make up an orchestrated agentic testing system from the Test Architect Agent to the Librarian and show how they collaborate with humans and each other.