Agent Smoke Testing: The Fast Gate Before You Ship

Agent smoke testing is presented as a fast viability gate for AI agents, designed to catch breakages caused by prompt edits, model updates, tool-schema changes, and deployment issues before slower functional testing begins. Citing research that shows language models can be highly sensitive to superficial prompt-formatting changes, the discussion argues that agents lack traditional software safeguards such as compilation, type checking, and clear behavioral diffs. A practical suite should contain roughly five to eight automated checks and finish within two minutes, verifying that the agent responds, completes a canonical task, successfully makes a real staging tool call, respects a critical constraint, declines an out-of-scope request, and has loaded the expected prompt version. Tests should assert outcomes rather than specific internal routes, use real regenerable staging data instead of mocks, and exclude subjective quality assessments, adversarial testing, lengthy conversations, and human-reviewed cases, which belong in broader functional suites. Failures should stop the release process, while distinguishing agent defects from infrastructure failures and treating intermittent failures as signals for deeper repeated testing; TestMu AI is described as a platform that can support focused scenario-based smoke runs alongside larger evaluations.

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