Scaling Quality in a Decision Intelligence Platform [Testμ 2026]

At Testμ Conf 2026, Aily Labs’ Hammad Ahmed described a “quality super agent” composed of seven specialized QA agents coordinated to test an AI decision-intelligence platform whose outputs, acceptance criteria, and coverage are inherently difficult to predict. The agents cover exploratory testing, resiliency, test creation, pull-request review, root-cause analysis, LLM-based critique, and bug fixing, while conventional tools such as Playwright, Appium, Jira, Confluence, Datadog, Sentry, Slack, and GitHub remain central to the workflow. To ground the agents, the team used specifications, manually documented business rules, and roughly 10,000 existing automated test cases as product documentation rather than executable tests, but found that excessive context increased hallucinations. It addressed this through “skill gating,” which restricts each agent to task-relevant domain knowledge and can reduce token costs. Quality is monitored through a customizable “pulse” combining measures such as AI accuracy, defect leakage, and tenant health, with proactive daily Slack briefings and on-demand agent runs intended to encourage adoption. Ahmed emphasized human feedback through Jira and Slack when agents make incorrect triage or root-cause decisions, and outlined future plans for linked agent workflows, self-healing tests, and broader orchestration, while acknowledging that many performance claims and outcomes were qualitative rather than supported by published metrics.

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