AI Trust and Governance in QE: Who Signs Off [Testμ 2026]

A Testμ Conf 2026 discussion featuring Infosys quality engineering CTO Harleen Bedi argued that humans remain accountable for software releases even when AI writes, runs, and evaluates tests. As AI systems introduce nondeterminism, model drift, AI-generated test artifacts, and unpredictable failure impacts, traditional coverage metrics and one-time quality gates are becoming insufficient. Bedi advocates a continuous governance layer built around explicit thresholds, traceability of AI decisions and prompts, and post-production observability to detect and mitigate drift quickly. She describes a shift toward pipeline-integrated controls and calculated risk, while enterprises standardize approved models and tools through centrally governed internal platforms. Testing AI agents requires multiple layers covering user-facing business outcomes, responsible AI concerns such as fairness and bias, security threats including prompt injection and jailbreaks, and agentic efficacy, which examines an agent’s decisions and tool-use path rather than only its final response. The discussion concludes that quality engineers can remain central by contributing domain expertise, developing AI evaluations, and building trust and governance practices for AI-enabled and agentic systems.

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