What Happens When You Go All-In on Agents [Testμ 2026]

At Testμ Conf 2026, Hinge Health’s Head of AI and ML, Rashi Agrawal, described how her regulated healthcare team used Claude Code, Cursor, and custom compliance-focused agent skills to eliminate a three-sprint QA backlog and reportedly reduce feature-development time by 60% while cutting PR cycle time from roughly a day to minutes. She argued that agents improve the full software delivery “outer loop” of planning, building, testing, reviewing, and shipping only when they operate within a robust harness of contextual information, restricted tools, guardrails, verification gates, and feedback mechanisms. Rather than replacing quality engineering, she said AI shifts its central constraint from producing software to exercising judgment about what is worth building, what is safe to release, and how quality should be defined for variable AI outputs. Her approach emphasizes risk-based earned autonomy, from fully supervised agents to autonomous operation within proven boundaries, with stricter human review and audit requirements for critical user paths and patient data. Agrawal also stressed that agents lack lived user experience and business context, citing missed scenarios involving device switching, time zones, accessibility, and emotionally distressed patients, while showing that detailed domain context enabled an agent to generate more targeted test cases. She encouraged QA professionals to develop practical familiarity with AI tools, measure their net value on limited tasks, and participate earlier in product decisions as quality architects and designers of verification systems.

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