orchestrating-swarms

Multi-agent orchestration coordinates specialized agents by defining, assigning, and tracking tasks with dependencies. You create a team, spawn agents for distinct review aspects, and use JSON-based messaging to pass findings back to a lead agent.

Multi-agent swarm orchestration uses divide-and-conquer patterns to manage complex workflows. You need it when tasks benefit from parallel execution, self-organizing task queues, or pipeline workflows with dependencies across multiple AI agents.

Yes, multi-agent orchestration supports in-process, tmux, and iterm2 backends for agent execution. These environments handle the execution layer while JSON-based messaging facilitates inter-agent communication across the team.

The best way to build pipeline workflows with dependencies is using a task orchestration system that defines and tracks inter-agent tasks. This framework manages task assignment and facilitates message passing between agents via inboxes.

Inter-agent communication relies on JSON-based messaging and agent inboxes. Failures typically occur when the execution backend is misconfigured or when task dependencies are not properly defined before spawning agents into the team.

Yes, self-organizing task queues require an agent execution backend. You must configure an in-process, tmux, or iterm2 environment to handle the execution layer before creating teams and assigning tasks.