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QA Center of Excellence

Geval6’s Testing Center of Excellence (TCOE) ensures we use the latest in Techniques, Processes and Tools to enable Software Testing as a service to our customers. As focus on quality rises, so does the need to perform testing in an industrialised and cost-efficient way.

TCOE in AI Era

01
What Are AI Agents?

Defining agentic AI and how they differ from traditional tools

02
Why AI Agents in Testing?

Key drivers and opportunities for QA teams

03
Core Capabilities

Self-healing tests, automation, and AI-driven execution

04
Real-World Use Cases

Regression, API, performance, and security testing

05
Tooling Ecosystem

Modern AI-powered QA tools and platforms

06
Challenges & Best Practices

Adoption strategies and key pitfalls to avoid

What Are AI Agents?

AI Agents are autonomous software entities that perceive their environment, reason over goals, plan multi-step actions, and execute them — with minimal human intervention.

Perception

Reads logs, test results, code diffs, UI states

Reasoning

Applies LLMs to decide what action to take next

Action

Executes tests, scripts, API calls, browser interactions

Reflection

Reviews outcomes and adapts strategy in real-time

WHY AI AGENTS IN TESTING?

Traditional Testing Challenges

  • Manual test creation is slow & error-prone
  • Regression suites grow but coverage shrinks
  • Flaky tests erode team confidence in CI/CD
  • UI changes break hundreds of tests overnight
  • Hard to scale across microservices & APIs
  • Skill shortage — not enough SDETs to keep up
→

AI Agent Advantages

  • Auto-generate tests from requirements & code
  • Self-heal broken selectors & locators instantly
  • Autonomous exploratory testing around the clock
  • Intelligent test prioritization based on risk
  • Natural language test authoring for all roles
  • Continuous learning from production behavior

Core Capabilities of AI Test Agents

!

Intelligent Test Generation

  • Parses requirements, user stories & specs
  • Generates BDD scenarios in Gherkin syntax
  • Produces unit, integration & E2E tests
  • Covers edge cases missed by humans
✓

Self-Healing Automation

  • Detects broken locators after UI updates
  • Infers correct selector using ML heuristics
  • Updates test scripts automatically
  • Reduces maintenance effort by up to 70%
★

Autonomous Exploratory Testing

  • Navigates app like a human tester
  • Identifies unexpected behaviour & flows
  • Detects visual regressions & a11y issues
  • Runs continuously in pre-prod environments

REAL-WORLD USE CASES

Functional Testing

Agents write and execute test cases from acceptance criteria. Auto-validate UI flows, form validations & business logic end-to-end.

API Testing

Discover endpoints from OpenAPI specs. Generate request payloads, validate schemas, detect breaking changes automatically.

Security Testing

Fuzz inputs, probe for OWASP vulnerabilities, check auth flows, and surface security regressions in every build.

Performance Testing

Synthesize realistic load scenarios, profile bottlenecks, and compare baselines across releases intelligently.

Regression Testing

AI prioritises tests by change impact. Run targeted regression suites in minutes instead of hours.

Accessibility Testing

Agents audit WCAG compliance, flag contrast issues, missing ARIA attributes, and keyboard navigation gaps.

HOW AN AI TEST AGENT WORKS

01

Input

Requirements, Code Diff, UI State

02

Analyse

LLM reasoning, Risk Assessment, prioritization

03

Generate

Test cases, scripts, test data

04

Execute

Run tests, Collect Logs, Capture Logs

05

Report

Bug Reports, Coverage Map, Feedback Loop

AI-Powered Testing Tooling Ecosystem

Portal implementation

Test Generation

AI pair programmer for unit & integration test stubs .

GitHub Copilot

E2E Automation

Natural language step authoring & self-healing selectors

Playwright AI Testim

Visual Testing

AI-powered visual regression and cross-browser comparison

Applitools
Geval6

Autonomous Testing

Cloud agent that learns and adapts tests to app changes

Mabl
Geval6

Unit Test Gen

Autonomously writes JUnit tests for Java code at scale

Diffblue Cover
Using Standard Building Blocks

Stable Automation

Uses ML to create robust locators & reduce test flakiness

Testim.io
Using Standard Building Blocks

Cognitive Testing

NLP-based test authoring with self-healing AI execution

Functionize

Smart Assertions

AI-assisted test maintenance, analytics & failure triage

Katalon AI

The Impact: AI Agents in Numbers

70%

Reduction in test maintenance effort

3×

Faster test cycle time with AI agents

85%

Fewer escaped defects to production

60%

Increase in overall test coverage

Test Automation Adoption Trend (% Teams with AI Testing)

2021
15%
2022
28%
2023
42%
2024
61%
2025
78%

Challenges & Best Practices

⚠️

Watch Out For

  • Hallucinated test cases that miss real bugs
  • Over-reliance on AI without human oversight
  • Black-box agents making debugging hard
  • Training data quality affects test accuracy
  • AI test maintenance costs can creep up
  • Regulatory compliance needs validation
✅

Best Practices

  • Start with AI-assisted, not autonomous testing
  • Establish a human review gate
  • Integrate AI into CI/CD pipelines
  • Feed production telemetry back to AI
  • Create a quality dashboard for AI outputs
  • Train QA teams on AI limitations

ROADMAP: ADOPTING AI AGENTS IN QA PRACTICE

Phase 1 — Explore

Month 1–2
  • Audit current test estate
  • Identify automation gaps
  • Evaluate AI tools (PoC)

Phase 2 — Pilot

Month 3–4
  • Run AI agent on regression suite
  • Measure coverage & flakiness
  • Train team on AI authoring

Phase 3 — Scale

Month 5–6
  • Integrate into CI/CD
  • Expand to API & performance
  • AI test review workflow

Phase 4 — Optimise

Month 7+
  • Autonomous exploratory agents
  • Use production feedback
  • Continuous agent learning
Geval6