How to Test AI Agents for Production by Deep Barot | The Agentic Quality Summit Atlanta 2026

AI agents are no longer just answering questions—they're making decisions, calling APIs, executing workflows, and interacting with enterprise systems. That changes how they need to be tested. In this session, Deep Barot, Founder & CEO of ContextQA, explores why traditional software testing approaches fall short for AI agents and introduces a practical framework for validating them before they reach production. In this video, you'll learn: Why AI agent testing is fundamentally different from traditional testing The seven key dimensions of AI agent validation How to test response accuracy, guardrails, hallucinations, and tool execution Why node-level workflow validation matters for multi-agent systems How to validate deterministic behaviour, context retention, and model drift Best practices for load testing, compliance, and production readiness A live demonstration of ContextQA's AI Agent Testing platform Whether you're building AI agents with Salesforce Agentforce, Amazon Bedrock, Azure AI Foundry, Google Gemini, OpenAI, Anthropic Claude, or a custom platform, this session provides practical guidance for validating AI systems before they go live. About ContextQA ContextQA is an AI-powered testing platform that helps engineering teams validate web, mobile, API, and AI agents from a single platform. With ContextQA, teams can: Generate AI-powered test scenarios automatically Validate response quality and factual accuracy Test guardrails and policy compliance Verify tool calls and API execution Detect hallucinations and model drift Execute adversarial and multi-turn conversation testing Perform load and performance testing Integrate AI validation into existing CI/CD pipelines Learn more: https://contextqa.com/ AI Agent Testing: https://contextqa.com/platform/ai-age... Book a demo: https://bit.ly/4hBX0br Chapters 00:00 Introduction & ContextQA overview 01:02 Why AI agent testing matters 01:33 The limitations of traditional AI agent testing 03:24 Why production-ready AI requires validation 05:00 Seven dimensions of AI agent testing 05:15 Node-level workflow validation 06:11 Guardrail enforcement 07:25 Hallucination detection 08:14 Deterministic validation 08:52 Context retention 09:58 Performance benchmarking 11:14 Bias & compliance 11:46 How ContextQA validates AI agents 13:20 Live product demonstration 14:19 Connecting an AI agent 15:00 Uploading specifications & generating personas 15:55 AI-generated test scenarios 17:48 Running AI agent evaluations 19:24 Load testing AI agents 19:48 Security & adversarial testing 20:19 Regression testing across model upgrades 20:51 Multi-LLM judging & confidence scoring 21:49 End-to-end AI agent validation with ContextQA 22:35 Enterprise deployment, security & compliance