agent-orchestrator

Manage task dependencies in multi-agent workflows by defining a Directed Acyclic Graph (DAG). This ensures agents run in the correct sequence while allowing parallel dispatch for independent tasks to maximize execution efficiency.

The best way to run parallel agent execution is using a multi-agent orchestration framework. It dispatches independent agents simultaneously, applies consensus protocols for output validation, and synthesizes overlapping results into a final report.

Failure recovery in multi-agent systems uses automatic retries with exponential backoff. Micro-checkpointing saves progress during execution, allowing the workflow to gracefully degrade or reassign failed tasks without restarting the entire process.

Consensus protocols can merge or select outputs from multiple agents working on overlapping problems. This validates results during parallel dispatch, ensuring the final synthesized output meets your consistency and quality requirements.

No prior workflow automation experience is strictly required, as the system handles DAG dependency resolution and exponential backoff automatically. However, defining complex task graphs benefits from understanding your specific research, analysis, or simulation workflow logic.