AI agents are different from traditional software because their outputs may vary even when the input is the same. QA therefore needs to validate not only whether an action succeeds, but also whether the agent chooses appropriate actions, follows instructions, uses tools correctly and produces reliable outcomes.
Why AI Agents Are Different
Traditional applications generally follow deterministic paths. AI agents can interpret a request, decide what to do, call tools, inspect results and determine their next action. This introduces additional sources of variability and failure.
What QA Should Test
Testing should cover the agent's responses, decision-making, tool selection, tool parameters, workflow completion, handling of incorrect inputs, recovery from failures, security boundaries and consistency across repeated executions.
Deterministic Testing Is Not Enough
A traditional assertion such as expected output equals actual output is often insufficient. AI systems may produce multiple valid responses. QA should therefore combine deterministic checks with evaluation criteria that measure correctness, relevance, safety, completeness and adherence to expected behavior.
Build an Evaluation Layer
An evaluation layer can score AI outputs against predefined criteria. Depending on the application, this can include rule-based validation, semantic comparison, LLM-as-a-judge evaluation, human review and domain-specific metrics.
Evaluate the Complete Agent Workflow
Do not test only the final response. Validate the complete workflow: user input, reasoning or decision process where observable, tool selection, tool parameters, external-system interaction, intermediate results and final response.
Handle Non-Determinism
Tests should distinguish between acceptable variation and genuine failures. Instead of requiring one exact response, define behavioral expectations and acceptable output boundaries wherever the application allows multiple valid answers.
Senior QA Perspective
“I would treat an AI agent as a probabilistic system with multiple observable and evaluatable layers. My test strategy would combine functional checks, tool-call validation, evaluation metrics, adversarial scenarios, regression datasets and production monitoring. The important shift is from testing only whether the software produced the expected output to evaluating whether the system behaved correctly within defined boundaries.”
Interviewer Follow-ups
- • How would you test an agent that uses multiple tools?
- • How would you evaluate an AI response?
- • How would you handle non-deterministic outputs?
- • What is LLM-as-a-judge?
- • How would you build an AI regression suite?
- • How would you test hallucinations?
- • How would you test prompt injection?
- • Which AI quality metrics would you track?