Agentic AI for Software Testing with ContextQA
Director of Product Engineering, ContextQANKNaveen Khunteta
Host, Naveen AutomationLabs
What separates assistive (generative) AI from goal-driven agentic AI in testing? In this live session with Naveen AutomationLabs, ContextQA's Chaitanya Deshpande demos how purpose-built agents read requirements, generate and de-duplicate test cases, crawl apps to self-script, auto-heal locators, and run root-cause analysis across web, mobile, and API.
Walk away knowing how to apply it
What the conversation covers
The evolution from quality control to assurance to engineering to quality intelligence
AI-first design vs legacy no-code tools bolting AI on (resident vs migrant)
Live demo: a requirement agent generating tests from a spec, skipping duplicates
Live demo: an autocrawl agent running a login-and-apply-leave flow from a prompt
On-premise deployment with your own LLM for data-restricted industries
Regression agent: migrating manual/BDD suites and surfacing must-run subsets
Auto-healing with a confidence score and six-context element handling
Cross-browser/device testing, API, accessibility, and auto/manual defect raising
The ideas worth remembering
Copy-pasting GenAI output fails because it lacks context; agentic AI understands the goal
ContextQA fine-tunes existing models for testing — and can run fully on-premise
The future QA is an augmented tester focused on quality strategy and decisions
Adapt early — technology augments skilled practitioners, it doesn't eliminate them
When automatic cars arrived, people said they'd rather drive themselves — now most have adopted them. Identify your goal and leverage AI accordingly.— Chaitanya Deshpande
Who you'll hear from
Chaitanya Deshpande
Director of Product Engineering, ContextQA
Naveen Khunteta
Host, Naveen AutomationLabs
See ContextQA in action
Go from watching to doing — spin up an AI agent and watch it test, self-heal, and report for you.