orchestration-execution

You coordinate multiple AI agents using a wave-based execution model that organizes tasks into sequential or parallel waves, ensuring dependency requirements are met before subsequent waves initiate.

Wave-based execution in multi-agent systems is a coordination mechanism that groups agent tasks into sequential or parallel waves, ensuring agents in later waves only start after prior waves complete required handoffs.

You monitor AI agent health by tracking status states such as working, done, blocked, or stalled, which enables dynamic status checking and initiates automated corrective actions for failure recovery.

Yes, agent orchestration supports complex task delegation by managing dependencies across sequential or parallel waves, ensuring robust agent coordination and automated failure recovery for multi-agent systems.

You prevent stalled workflows by actively monitoring teammate health states, tracking working or blocked statuses, and initiating automated failure recovery actions to dynamically resolve stalls.

The best way to manage automated failure recovery is implementing a wave-based execution model with active teammate health monitoring, which tracks agent status and initiates corrective actions when failures occur.