Five Common Myths About AI Coding Agents and How to Verify Them

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A developer guide published on DEV Community identifies five widespread misconceptions about AI coding agents that can mislead engineers into trusting unverified outputs. These myths include beliefs that a logged tool call confirms a file was actually written, that files persist on ephemeral cloud runners after a session ends, and that a model narrating 'done' means tests genuinely passed. The guide also warns that tool schemas are not automatically portable when switching AI models, and that indefinite retry loops will eventually fix a broken repository without a proper success oracle. For each myth, the author provides specific shell commands and Python scripts that developers can run locally to verify real disk state, exit codes, and schema compatibility rather than relying on agent chat logs or UI indicators.

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