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Developer Builds AI Posting Assistant, Discovers It Fabricated a Promise From Test Data

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A developer building an AI-powered content assistant for LinkedIn found the agent misread internal Zapier validation posts as public content, incorrectly inferring an unfulfilled promise to followers. The tool was designed to analyze recent post history and suggest three topic angles rather than generating a draft immediately. During early testing, the agent treated its own interpretations of historical data as confirmed facts, a flaw the developer addressed by adding a rule requiring the agent to flag inferences and seek confirmation. A secondary gap emerged when the agent suggested covering a topic the developer had already written about elsewhere that same morning, since the skill had no visibility into external platforms. The experience highlighted two practical pitfalls in agent design: polluted training data can produce confident but false conclusions, and an agent's awareness is strictly limited to the data sources explicitly provided to it.

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