Why Traditional Testing Fails
Traditional software tests assert exact outputs: given input X, expect output Y. AI agents don't produce exact outputs. They produce variable outputs that might be equally correct. An agent asked to summarize a document might produce two different summaries on two runs, both accurate and useful. An assert-equals test would fail on the second run.
This non-determinism, which we discussed earlier, makes standard unit testing largely inapplicable. You can test individual tool calls, parameter validation, and error handling deterministically, but you can't test the agent's overall behavior with exact assertions.
What Works Instead
Property-based testing checks that outputs have certain properties rather than exact values. "The summary should be less than 200 words," "the result should contain at least 3 of these 5 key facts," "the generated SQL should be syntactically valid." These assertions accommodate variability while still catching problems.
Statistical testing runs the same scenario multiple times and checks success rates. If an agent succeeds 19 out of 20 times on a test scenario, that's useful information. If it succeeds 12 out of 20 times, you know there's a reliability issue worth investigating. Single-run tests miss these patterns.
Criteria-based evaluation uses a set of quality criteria scored independently. Did the agent use the right tools? Did it handle errors gracefully? Was the output well-formatted? Was the conclusion supported by the data it gathered? Each criterion can be checked separately, giving you a nuanced quality assessment.
The Minimum Viable Test Suite
At minimum, test your agent on: a typical success case, a case where a tool call fails, a case with ambiguous input, and a case that requires multiple steps. These four scenarios cover the most common failure modes and can be run in under five minutes. It's not comprehensive, but it's better than no testing at all, which is unfortunately the norm for most agents in production.
Related Reading
- Testing AI Skills: Approaches That Actually Work
- Why Agent Benchmarks Rarely Reflect Real-World Performance
- How to Debug an AI Agent That Keeps Making Mistakes
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