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Testing AI Agents in Production: A New Playbook for QA Teams

64 min AI Agents 2 speakers
Webinar
HNHarsh Nigam
From ContextQANKNaveen Khunteta
Host, Naveen AutomationLabs

In this fourth live session with Naveen AutomationLabs, guest Harsh Nigam walks through how QA teams should test AI agents before they reach production. He covers why agents are non-deterministic, how to design test cases first, and how to use personas, guardrails, LLM judges, and red teaming to ship agents with confidence instead of catastrophic failures.

What you'll learn

Walk away knowing how to apply it

How to test non-deterministic AI agents instead of treating them as a black box
Why you should design test cases before building the agent or its system prompt
What guardrails, fallbacks, and kill switches to add for production reliability
How personas, use cases, and dynamic scenarios turn into thousands of test cases
Why multiple LLM judges are needed to reduce bias and non-deterministic scoring
How to run red teaming, load testing, and drift comparison across test runs
Inside this session

What the conversation covers

Why almost no one is testing AI agents, and where enterprises are stuck today

Chatbot and agent behavior as non-deterministic systems versus traditional apps

Why 100 percent coverage is impossible, and the role of guardrails and compliance

Test-case-first strategy: define what the agent must not do before what it should

Connecting an agent, uploading an overview doc, and generating personas and use cases

Static versus dynamic test cases and simulating long multi-turn conversations

Configuring LLM judges, pass ratios, determinism runs, red teaming, and load testing

Reading reports, comparing runs for drift, and keeping a regression cycle alive via MCP

The QA role, third-party testing, model choice, and the cost of getting it wrong

Key takeaways

The ideas worth remembering

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The creator is the worst checker, so agents need independent third-party testing that does not expose internal prompts and reduces bias.

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Start with test cases, not code. Define what the agent must never do, then build and iterate until accuracy hits your target.

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Use at least two judges, ideally three, and average them, since a single LLM judge can be randomly strict, lenient, or wrong.

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Do not be scared of AI agents. Build them, test them thoroughly with guardrails, then release. Do not skip the middle step.

“
Don't be scared. Build them, test them, and then release them. Don't skip the middle part.
— Harsh Nigam
Speakers

Who you'll hear from

HN

Harsh Nigam

From ContextQA

NK

Naveen Khunteta

Host, Naveen AutomationLabs

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