Why smaller tasks and tighter gates are key to reliable AI dev workflows

A DEV Community analysis argues that the most critical decision in an agentic development workflow is determining the size of the task handed to an AI agent before a run begins. The author recommends deliberately smaller, narrower work items, noting that larger tasks erode review quality, turn approval gates into rubber stamps, and make plans vague and unverifiable. Effective task descriptions must include specific acceptance criteria, relevant context the agent cannot discover on its own, and direct pointers into the codebase. Gates are framed not as overhead but as the core product of the workflow, since any error approved at a gate propagates faithfully through every downstream step. The piece acknowledges the trade-off — more items mean more runs and more overhead — but contends this cost is worth accepting to preserve meaningful human control.