Model Migrations Can Silently Break AI Agent Behaviour Without Any Code Changes

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Switching the underlying model in an AI agent system can dramatically alter planning behaviour even when the system prompt, tools, and task remain identical. The core issue is that different models vary in how much reasoning they externalise as separate turns versus handling internally within a single response. This makes iteration-based loop guards unreliable, as they measure a model's formatting habits rather than actual work completed. Common symptoms include fewer turns with skipped verification steps, unexpected cost spikes, parallel tool calls collapsing multiple steps into one, or downstream parsers failing when expected narrative blocks disappear. Developers are advised to treat turn count as a proxy metric that must be recalibrated after any model migration rather than a stable measure of agent progress.

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