Testing autonomous agents is different from testing traditional software. You’re not just verifying outputs—you’re observing behavior over time.
One small hack that helped me early on was simplifying everything around the agent. The more variables you remove, the easier it is to understand what’s actually going wrong.
That includes data formats.
When testing, I avoid feeding agents “real” files unless necessary. I convert everything into a minimal, consistent representation first. This reduces noise and makes failures easier to attribute to logic instead of input quirks.
Another habit I picked up is keeping preprocessing steps separate from the agent itself. If a test fails, I want to know whether the issue came from reasoning or from data preparation.
I’ve noticed more developers sharing similar habits as agent systems mature. Some of these workflow discussions show up in broader AI agent communities like the ones documented at https://moltbook-ai.com/
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These hacks aren’t groundbreaking, but they make iterative testing far less painful.
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