Agentic Workflow Testing
Agentic workflow testing validates an end-to-end multi-step process an AI agent plans and executes on its own — breaking a goal into subtasks, choosing an order, acting, and adjusting based on results — rather than testing a single prompt-response exchange.
Unlike a scripted workflow where the steps are fixed in advance, an agentic workflow's steps are decided by the model at runtime, which means the same starting goal can legitimately produce different step sequences on different runs. Testing has to validate that the outcome is correct and that the path taken was reasonable, not that a specific sequence of steps occurred.
A practical approach is defining the workflow's non-negotiables separately from its variable parts — e.g. "must verify the user's identity before taking a destructive action" is a hard invariant to assert on every run, while the exact order of information-gathering steps beforehand can vary.
Long workflows are also where cost and latency testing overlaps with correctness testing: an agent that eventually gets the right answer after 40 unnecessary tool calls has a workflow bug even though its final output would pass a pure accuracy check.
