Scaling Quality in a Decision Intelligence Platform: The Agentic QA Playbook | TestMu 2026
What does it take to build a quality-first culture inside a fast-moving AI company serving Fortune 500 enterprises — where the product itself is made of AI agents?
At Aily Labs, we build a Decision Intelligence platform that helps global enterprises across Pharma, CPG, and Retail make better decisions, faster. Our platform doesn't just surface data — it reasons, advises, and acts. And that changes everything about how you test it.
Traditional QA was built for deterministic systems: you define inputs, you assert outputs, you ship. But when your product is a network of AI agents that reason, collaborate, and adapt — the old playbook breaks. There are no fixed outputs to assert. There are no scripts that can anticipate emergent behavior. And when something goes wrong for a Fortune 500 client, "we didn't have a test for that" is not an answer.
So we rebuilt QA from the ground up — not as a safety net at the end of delivery, but as an intelligent, always-on system woven into every layer of how we build and operate. We gave quality a pulse. We taught agents to test agents. And we made AI the default way our QA team works.
This talk walks through that transformation — and the three things every QA engineer should take back to their team.
Quality Needs a Pulse, Not Just a Report: How we built a real-time quality accountability system — tracking AI accuracy, defect leakage, and tenant health in one place — so quality degradation is visible to everyone, instantly, not discovered in a post-mortem.
Agents Orchestrating Agents — The New QA Architecture: How we built a network of collaborating AI agents (triage, root cause, data validation, exploratory) that hand off context, escalate findings, and coordinate autonomously — replacing manual test coordination with an always-on quality engine.
AI as the Workflow, Not the Shortcut: How AI is embedded into every layer of day-to-day QA work — from defect triage and ticket writing to on-demand exploratory runs triggered from chat — and what the shift from "engineer who uses AI" to "engineer whose entire workflow is AI-augmented" actually looks like in practice.

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