Master Agentic QA in this 3-hour hands-on course. Move beyond traditional testing to continuous validation of agent reasoning, evidence, and safety. Learn daily QA loops, risk-tiered validation, production QA stacks, and ROI-driven metrics using proven patterns from real-world enterprise deployments. Based on AEBOP T4.2.
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Traditional QA collapses when cognition enters the loop. Agentic systems fail differently—through plausible but ungrounded reasoning, silent drifts, and judgment errors. This module establishes why AQA exists: to close the trust gap by verifying not just what agents produce, but how and why they decide.
You'll learn the five dimensions of AQA and the maturity ladder from L1 (semantic replay) to L5 (self-testing autonomy). Through real case studies, you'll quantify the business impact of unverified cognition and assess your organization's starting point. This foundation turns quality from a retrospective check into a forward-looking control system.
AQA operates as a daily heartbeat, not a quarterly audit. You'll master the 7-step QA loop: capturing telemetry, validating reasoning, measuring drift, triaging anomalies, reporting evidence, feeding back to AgentOps, and resetting. This is the runbook that keeps autonomy bounded and improvable under real-world conditions.
Not all errors are equal. You'll implement risk-tiered validation, applying strict human-reviewed checks to high-risk flows (regulatory/safety) while automating low-risk validation. Learn five field-proven best practices—from replay QA for regression detection to self-testing patterns—and build triage playbooks with clear escalation paths and SLAs.
Assemble the minimum viable QA stack: composable, observable, and auditable. You'll define the non-negotiable data contract (JSON trace schema), wire the linear data path from telemetry to dashboards, and implement CI/CD risk gates that block dangerous changes. The one-week bootstrap plan gets you from zero to evidence in days.
Choose and combine design patterns for your context: validator-in-the-loop for real-time control, shift-left CI/CD gates, streaming continuous QA, and supervisor-based auto-correction. Learn assembly recipes for starter, continuous, regulatory, and advanced deployments, each with acceptance checks to ensure your architecture produces verifiable trust.
AQA sustains through team rituals, not just technology. Establish the four core roles (QA Lead, AgentOps, ML Engineer, Governance) and implement the cadence of daily standups, weekly reasoning audits, monthly trust reviews, and quarterly maturity workshops. Learn from cross-industry field lessons in healthcare, finance, and manufacturing.
Prove AQA's value with the metrics stack and ROI model. Track reasoning quality (grounding ratio), operational integrity (drift detection latency), and economic impact (rollback cost avoided). Identify and recover from critical anti-patterns—like tool sprawl or unowned alerts—and use implementation checklists to achieve operational readiness with measurable trust.