The New QA Paradigm:
Agentic QA vs. AI Agent Testing
The QA playbook just got rewritten—and most engineering teams are confusing two fundamentally different things. AI is fundamentally altering how we think about quality and evaluation systems.
Rama — Founder & Principal AI Architect
With 20+ years of engineering experience, Rama specializes in non-deterministic test frameworks and production LLM evaluation pipelines.
Where Does the AI Sit?
Agentic QA
Using autonomous AI agents as the tester to augment the QA lifecycle: auto-generating test scripts, self-healing broken UI flows, triaging logs, and running adaptive exploratory checks.
AI Agent Testing
Putting autonomous agents on the operating table as the System Under Test (SUT): evaluating probabilistic behavior, trajectory paths, tool-call accuracy, and non-deterministic outcomes.
💡 Key Questions We Will Answer Live
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1
How do you actually build a coherent test strategy for non-deterministic AI systems?
Moving beyond traditional static test cases to handle probabilistic model outputs and dynamic agent trajectories.
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2
What does the new AI Test Pyramid look like when pass/fail boolean assertions aren't enough?
Redefining evaluation metrics, golden datasets, LLM-as-a-judge scoring, and hallucination guardrails.
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3
Is it needed to learn specific tools or what's the best way to get trained?
A practical roadmap for engineers, SDETs, and leads looking to upskill and transition to AI Quality Engineering.
Who Should Attend
Automation Test Engineers, SDETs, QA Leads, Software Developers, AI Product Managers, and Engineering Managers looking to understand the future of AI testing and quality engineering.