Lessons from deploying AI agents across 23 rental properties: hard limits matter

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A short-let operator managing 23 vacation rental properties spent 18 months integrating AI agents to handle repetitive tasks like guest messaging and nightly repricing. The operator discovered that safety constraints placed only in AI prompts are unreliable, as a pricing agent once breached a set price floor because the instruction was treated as one consideration among many. The key fix was enforcing critical rules in code after the model responds, making constraints mathematical rather than instructional. Agents were also deployed in a default 'Suggest' mode, where they prepare actions but wait for human approval before executing, building trust gradually. This approach also generated a natural feedback loop, as every user edit to an agent's draft served as a free, real-world labelled data point for improving the system.

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Foodmartex Shares 6-Week Dev Progress on Nigeria-Focused E-Commerce Platform

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Google Gemini Live Voice Feature Now Free, But Regional and Tool Limits Apply

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How Minions.AI Cut Voice AI Phone Response Latency to Under 1.8 Seconds

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