The Hard Part of Long-Running Agents Is Not the Model

说明与用途

长时间运行的 AI 视频代理失败源于系统设计，而非模型质量。关键改进包括显式状态跟踪、将模型建议与确定性代码分离、健壮的预算管理和无状态协调。

问题

Long-running AI agents for video production fail not because of model quality but due to system design flaws that mismanage state, budget, and execution flow.

结果

By treating the agent as a distributed system with explicit state management, deterministic execution, and robust budgeting, NoodleTomato achieved reliable, resumable, and cost-effective long-running video production.

工作流

• 1. Receive brief and create initial plan
• 2. Model proposes shot list, narration, and render steps
• 3. Code validates proposals, checks permissions, reserves budget, writes committed state
• 4. Workers execute tasks, report status to artifact store
• 5. Coordinator remains stateless, wakes on job completion
• 6. After all tasks, run validation and audit outside agent control
• 7. If needed, replay state for debugging without re-running paid jobs

证据

The model did not hallucinate. It reasoned correctly from an incomplete snapshot.

来源

The model should never grant itself a capability, approve its own output, or author the amount shown on a billing card.

来源