Agentic AI Changed What We Test, And How We Test It [Testμ 2026]

At Testμ Conf 2026, Pooja Oza argued that conventional exact-match testing is inadequate for agentic systems because identical prompts may produce different yet valid execution paths, proposing instead a “bounded behaviour envelope” that evaluates whether an agent achieves the user’s goal, remains within authorization, business and safety rules, and grounds its decisions in verifiable facts. She emphasized that fluent responses are not proof of correctness, illustrated by a fabricated 90-day return-policy answer, and recommended assessing outcome, grounding, action sequences, policy compliance, and reliability rather than relying on binary pass/fail results. The session also warned that ungoverned self-healing automation can create “false greens” by repairing selectors without confirming business outcomes, while governed healing should verify database states, receipts, logs, and traces. AI can help generate test cases, identify edge cases, and analyze failures, but Oza maintained that humans must define risk boundaries, approve consequential actions, and make final release decisions. Using a conceptual media-player testing model, she showed how trajectory-based validation could verify playback timing, ad telemetry, policy compliance, and observability evidence under changing conditions, while her Q&A noted that her organization remains exploratory in agentic testing and that mindset, tool churn, cost control, integration risk, and human accountability remain significant practical concerns.

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