Agentic coding tools like Cursor, Claude Code, and OpenAI Codex need better message queueing options for users. Peter Steinberger's experience highlights a frequent issue: models like GPT-5-Codex often stop generating output at random checkpoints, even with explicit instructions to finish tasks. To work around this, users may need to queue messages, ensuring the model continues to produce the desired output without interruptions. Queued messages can also enhance the context for tasks. For instance, when troubleshooting a bug in a specific component, users can queue messages to explain how that component works before diving into diagnostics. This extra context improves the model's ability to analyze and provide accurate insights. After discussing a design in dialogue, users can outline implementation steps and even commit changes through a series of queued tasks. This approach makes routine actions more efficient, especially when users feel confident about the next steps. Different coding tools handle message queuing in various ways. OpenAI Codex uses post-turn queuing, where new messages wait until the current task is finished. Claude Code, on the other hand, uses boundary-aware queuing, allowing interruptions after natural breaks in the action. The article suggests that integrating all queuing methods into these tools could significantly enhance productivity, especially for users managing multiple agents simultaneously. A simple key combination or menu option could allow users to switch between queuing styles based on their needs. The author warns against vague commands like "queue continue" because it can lead to unexpected output. Instead, specificity is key. For example, instructing the model to "continue handling the linting errors until none remain" helps maintain focus on the task at hand.