orchestration-coordination-framework

Coordinate AI agents using orchestration patterns like DAGs, state machines, and event-driven designs. This Skill provides frameworks to manage task dependencies, agent routing, and fault tolerance across production-scale workflows using tools like Airflow, Temporal, Prefect, Celery, or Step Functions.

DAG orchestration expresses linear task dependencies with clear inputs and outputs; state machine orchestration models workflows as transitions between states with guards and conditions. Use DAGs for sequential pipelines and state machines for complex decision logic and agent coordination patterns.

Implement retry logic, circuit breakers, and error handling at the orchestration layer to catch failures and replay tasks. This Skill covers patterns for managing transient failures, cascading errors, and recovery strategies across distributed agent systems.

Yes. Event-driven coordination routes agent tasks through message buses, allowing asynchronous, loosely-coupled workflows. This approach scales better than DAGs for high-concurrency scenarios and supports real-time task triggering and complex branching.

Monitor and trace distributed workflows to track agent execution, task state transitions, and failures. This Skill covers instrumentation patterns for logging, metrics, and distributed tracing to debug multi-agent systems at scale.

This Skill applies orchestration patterns across Airflow, Temporal, Prefect, Celery, and Step Functions. Each platform implements these patterns differently; the Skill covers the underlying design principles and adaptation strategies for your chosen tool.