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How developers keep AI agent prompts and tools reliable when switching models

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A developer raises the problem that an AI agent's behavior can shift when the underlying model changes, even though the code itself stays the same. A prompt that performs well on one model may produce different results on another, and tool calls that were dependable may begin failing without obvious warning. The post asks readers how they detect such regressions, including whether they test agent behavior or learn of problems from users. It also probes how prompts, tool definitions, and test cases are organized, and what steps people take before upgrading a model. The author invites developers to share their solutions or admit they have not yet found one.

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