Debugging AI systems is fundamentally different from debugging traditional software because generative AI is probabilistic rather than deterministic. Unlike traditional code that produces the same output every run, LLMs can return different answers each time. In agentic systems, these non-deterministic decisions compound across multiple LLM calls and tool invocations, causing execution trajectories to diverge widely between runs. The recommended approach is running systems in production multiple times to observe behavior patterns and placing guardrails around undesired outputs, accepting that non-determinism cannot be fully eliminated.
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