Developer Builds Agent-Agnostic AI Coding Workflow With Enforced Verification Gates

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A developer writing for DEV Community has shared an AI coding workflow designed to work across multiple agents rather than relying on any single tool. The process runs through defined stages: a 'council' of agents independently analyzes a problem and produces a detailed technical issue before any code is written, minimizing vague instructions that lead to misbuilt features. A separate agent then handles implementation, followed by an automated review-and-fix cycle to prevent self-congratulatory self-review. A key rule enforces that agents must provide real proof of functionality — such as screenshots or query results — before code can be pushed, since passing tests alone is not considered sufficient evidence. The author credits the approach to influences including Lauren Tan, Peter Steinberger, and Matt Pocock, and applies the workflow across several personal projects.

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