You Can't assertEquals an AI Agent [Testμ 2026]

At Testμ Conf 2026, Microsoft’s Gaurav Khurana argued that testing AI agents should focus on their execution trajectory rather than their final text, since correct-looking responses can conceal skipped policy checks, incorrect tool use, failed backend actions, or misrouted requests. He described agents as models operating in loops with tools and introduced a five-layer framework covering reasoning, tools, memory, orchestration, and outcomes, using a refund-agent example to show how testers can inspect model turns, tool-call order and arguments, user-thread isolation, service-failure handling, recursion limits, routing, and backend refund records. Khurana emphasized testing with multiple users, deliberately simulating service outages, validating claims against source systems such as ledgers, and using telemetry to reveal behavior that UI-based tests cannot detect. Because agent responses are non-deterministic, he recommended semantic evaluation against ground-truth datasets and LLM-as-a-judge approaches instead of exact string comparisons, while noting that evaluation tools such as agentevals, Microsoft Agent Framework, DeepEval, and LangGraph should be selected according to team needs.

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