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Developer Builds AI Agent Workflow That Self-Classifies Tasks Without Human Prompting

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A software developer writing for DEV Community has shared how a nine-part project to build a structured AI agent workflow evolved beyond its original scope. The system was designed to address a recurring problem: AI agents either move too fast and produce unchecked errors, or require constant human oversight to stay on track. Rather than manually enforcing workflow phases, the developer discovered the agent consistently self-classified tasks — routing trivial fixes inline and running complex work through a full multi-phase cycle. This behavior was driven by a short instruction file at the repository root, supported by seven skill files, artifact slugs, a requirement manifest, and exit gates. The result placed the developer in a new role — neither writing every line nor reviewing finished output blindly, but monitoring each phase gate as artifacts passed through the pipeline.

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Developer Builds AI Agent Workflow That Self-Classifies Tasks Without Human Prompting · ShortSingh