Agentic AI and the Next Decade of Quality Engineering [Testμ 2026]

At Testμ Conf 2026, Accenture quality engineering leader Mallika Fernandes described agentic AI as a shift from traditional testing toward “trust engineering,” in which teams evaluate not only software applications but also the probabilistic agents that generate tests, scripts, data, defect reports, and risk assessments. She traced quality engineering’s evolution from manual and scripted testing through AI-assisted workflows to autonomous agents, arguing that repetitive work will increasingly be automated while human engineers focus on strategy, end-to-end system understanding, oversight, and accountability for outcomes. Fernandes recommended treating AI as an enthusiastic intern or pair programmer whose work is valuable but must be checked, placing the ideal level of reliance at a stage where teams actively review and revise outputs rather than accepting them unquestioningly. She warned that automation bias, skill atrophy, cognitive surrender, data exposure, security risks, and model-token costs can undermine autonomous-testing initiatives, and emphasized guardrails, human review, and cost control. She also argued that AI enables a more predictive form of shift-left testing by identifying likely defects during requirements and user-story stages, while suggesting that the lasting value of quality professionals will lie in governing, auditing, and building trust in AI-driven delivery systems.

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