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AI Across the Testing Pyramid
A Practitioner's Field Guide to AI-Augmented Quality Engineering
This book is 100% completeLast updated on 2026-11-06
A short, tool-agnostic field guide to what AI really changes at the unit, integration and end-to-end levels of testing, and how to adopt it without losing trust in your suite.
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About
About the Book
Your test suite is bigger than ever. Do you trust it more?
AI can now write tests, heal broken selectors, triage failures and even open pull requests. Each of those capabilities also has a way of failing quietly: with a green check, not a red one.
AI Across the Testing Pyramid is a short field guide for QA engineers, SDETs and engineering leads who want to use AI in testing without losing trust in their suite. It walks the testing pyramid level by level and asks, at each one, what AI really changes and what it only appears to change.
Inside:
• The four costs of a test (authoring, execution, maintenance, diagnosis) and which ones AI really lowers
• Unit level: using models as adversarial reviewers, plus property thinking and mutation testing made practical
• Integration level: catching contract drift and stale mocks before production does
• End-to-end level: the resilience-vs-sensitivity trade-off behind self-healing tests
• The cross-cutting layer: flakiness, failure triage, test selection, suite health
• Agentic pipelines: taking a ticket to a pull request with bounded loops and human gates
• Risks and governance: automation bias, non-determinism, provenance
• A step-by-step adoption roadmap for your team
Tool-agnostic. No vendor pitches. Written by a Senior SDET who builds AI-assisted test pipelines for U.S. engineering teams.
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About the Author
Santiago Rodríguez Gutiérrez
Santiago is a Senior SDET and QA Automation Engineer based in Medellín, Colombia, working with U.S. engineering teams on product quality, test automation, and data pipeline validation. He has spent close to a decade in quality engineering, designing automation strategies, building frameworks from scratch, and running workshops to teach automation inside the teams he works with.
His work spans every level of the testing pyramid: unit and component testing, contract testing for Kafka events and REST services, end-to-end suites in Playwright and Cypress, and the reconciliation of large data pipelines. More recently he has focused on AI-augmented QA, using models to analyze and test data reconciliation processes, and designing agentic pipelines that connect a ticketing system, a test framework, and version control through MCP integrations, with human review at every gate.
He writes about practical, hype-free AI in testing on LinkedIn.
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