Developer Builds Governed Workflow to Stop Coding Agents Accumulating Junk Memory

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A developer named Lars has released an open-source tool called voku/agent-loop that addresses a common problem with AI coding agents: the gradual accumulation of low-quality, undifferentiated context that degrades performance over time. Rather than relying on a single growing memory file, the workflow separates context into three distinct levels — temporary session state, unreviewed findings, and human-approved project guidance. Each coding task begins with an explicit work brief covering goals, permitted scope, affected files, and required validation steps, all of which must be formally approved by a human before the agent proceeds. The system also uses separate tools for recalling relevant rules and mapping code structure, avoiding the need to load entire repositories into the prompt. The project is publicly available on GitHub, along with an interactive demo illustrating the governed task lifecycle.

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