Engineering Quality for the AI Era
From Test Automation to Intelligent, Agentic Quality Engineering
Read White Paper (opens in a new tab)This page describes IQE Labs research. Advisory and consulting engagements are described separately under Solutions.
Research
Researching the next generation of Quality Engineering
Our research examines how Quality Engineering must evolve as systems become more automated, data-intensive, AI-enabled and agentic. We combine practitioner perspectives, engineering experiments and structured models to explore how organizations can build greater confidence in increasingly intelligent software.
Featured Publication
Focus Areas
Intelligent Quality Engineering
How intelligence, evidence and continuous learning can strengthen engineering confidence.
Agentic Quality Engineering
Evaluating and governing systems that reason, plan, use tools and take actions within defined boundaries.
AI System Evaluation
Moving beyond simple expected-vs-actual validation toward behavioral and evidence-based evaluation.
RAG Quality Engineering
Evaluating retrieval, context quality, grounding and system behavior in RAG-based applications.
Quality Evidence & Governance
Engineering traceability, controls and evidence into intelligent systems.
Enterprise QE Transformation
Helping organizations understand and evolve their Quality Engineering capabilities for AI-enabled delivery.
Our Research Approach
How IQE Labs develops its research
We distinguish between established external evidence, practitioner observations, experimental findings and IQE Labs proposed models. Research is continuously refined as new evidence and practical experience emerge.
- 01
Practitioner Input
- 02
Engineering Experiments
- 03
Evidence Review
- 04
Model Development
- 05
Publication
- 06
Continuous Refinement
Questions shaping our research
Does a fully green test suite reliably indicate release confidence?
What evidence is required to establish confidence in AI-enabled systems?
When systems can reason, use tools and take actions, is validating only the final output enough?
How should governance evolve as Quality Engineering and AI systems become increasingly autonomous?
Research Library
Research & Insights
White Papers
Engineering Quality for the AI Era
From Test Automation to Intelligent, Agentic Quality Engineering
Read White Paper (opens in a new tab)Engineering Quality for Agentic AI Systems
From Output Validation to Behavioral Evidence, Control and Trust
IQE Labs Insights
Why Quality Engineering Must Change in the AI Era
Experiments & Technical Research
Engineering experiments and technical findings will be published here as they reach an appropriate level of evidence and documentation.
Research-Led, Practitioner-Informed
From research question to practical method
- 01Research
- 02Experiment
- 03Develop
- 04Apply
- 05Refine
Learning from application feeds the next research question.
Contribute to the research
Are you working through Quality Engineering challenges involving AI, automation, data, governance or increasingly autonomous systems? We welcome practitioner perspectives that can help challenge and refine the research.