An Architecture for Long-Horizon Agents: Levels, Ticks and Cascaded Intelligence

TL;DR

A new architecture for long-horizon agents enables continual learning by maintaining context over extended periods. This approach successfully reproduced a reinforcement-learning result over ten days with minimal human intervention.

Key Takeaways

• Implement time-indexed levels to maintain context over long tasks.
• Use a clocked tick for structured autonomous actions.
• Incorporate cascaded intelligence for task escalation.
• Design systems that allow for continual learning without model weight changes.